Annamalai Manickavasagan
Hemantha Jayasuriya Editors
Imaging with
Electromagnetic
Spectrum
Applications in Food and Agriculture
Imaging with Electromagnetic Spectrum
Annamalai Manickavasagan
Hemantha Jayasuriya
Editors
Imaging with
Electromagnetic Spectrum
Applications in Food and Agriculture
13
Editors
Annamalai Manickavasagan
Hemantha Jayasuriya
Sultan Qaboos University
Muscat
Oman
ISBN 978-3-642-54887-1
ISBN 978-3-642-54888-8
DOI 10.1007/978-3-642-54888-8
Springer Heidelberg New York Dordrecht London
(eBook)
Library of Congress Control Number: 2014939534
© Springer-Verlag Berlin Heidelberg 2014
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Preface
As we are in the midst of the information technology era, the rapid development
in electronics and instrumentation areas has made it possible to create informative
images using each and every micro region of the light spectrum at high resolution and speed. Imaging with each region has its own merits, and widely used in
diversified fields. This technology is also being well utilized in agricultural and
food sector for various applications. However, there are lots of unexplored areas of
applications for the improvement of agricultural and food production system using
existing imaging technology. Through this book, we have made a resourceful compilation for theory, instrumentation, practical considerations on readily utilized,
and potential applications for the whole light spectrum in an easily understandable
manner.
This book begins with a prologue chapter providing an introduction to the electromagnetic spectrum, from the imaging point of view, by presenting example
cases where different bands of frequencies are used in different areas of research.
This is also fortified with descriptions closely inclined to food- and agriculturerelated examples. Out of the remaining nine chapters, each one is dedicated for
specific and useful wavelength regions in the spectrum: gamma ray, X-ray, ultraviolet (UV) light, visible light, near infrared (NIR), mid and far infrared, thermal
infrared, microwaves, and radiofrequency waves. Around 100 images have been
used throughout this book to visualize theory, instrumentation, equipment, and
various applications thoroughly. This book is prepared carefully by considering
wider range of audiences such as high school, undergraduate and graduate levels,
academics, and researchers in various disciplines of agricultural and food-related
systems. This comprehensive collection will certainly be beneficial to students,
researchers, academics and the others who are all involved or interested in agriculture and food.
Annamalai Manickavasagan
Hemantha Jayasuriya
v
Acknowledgments
The editors would like to sincerely thank the Sultan Qaboos University for providing facilities and The Research Council (TRC) of Sultanate of Oman for funding
a research project on Imaging Application (Project No. RC/AGR/SWAE/11/01Development of Computer Vision Technology for Quality Assessment of Dates
in Oman), which encouraged the editors to initiate compiling this book. All chapter authors are whole heartedly acknowledged for prompt submission according to the deadline. The support provided by Dr. P. M. K. Alahakoon, University
of Peradeniya, Sri Lanka, during the early stages of this book project is highly
acknowledged. We all thank the staff of editorial and production department of
Springer for their unstinted support and efforts to bring this book in the present
form.
vii
Contents
1 Introduction to the Electromagnetic Spectrum. . . . . . . . . . . . . . . . . . . Sindhuja Sankaran and Reza Ehsani
1
2 Gamma-Ray Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Manickavasagan and N. Yasasvy
17
3 X-ray Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Karunakaran and D. S. Jayas
33
4 UV Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preetam Sarkar and Ruplal Choudhary
57
5 Visible Light Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neetha Udayakumar
67
6 Near-infrared Imaging and Spectroscopy. . . . . . . . . . . . . . . . . . . . . . . . V. Chelladurai and D. S. Jayas
87
7 Mid- and Far-infrared Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Sindhuja Sankaran, Lav R. Khot and Reza Ehsani
8 Thermal Infrared Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
M. Teena and A. Manickavasagan
9 Microwave Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Massimo Donelli
10 Radio Frequency Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Gabriel Thomas and A. Manickavasagan
ix
Contributors
V. Chelladurai Department of Biosystems Engineering, University of Manitoba,
Winnipeg, MB, Canada
Ruplal Choudhary Department of Plant, Soil and Agricultural Systems, Southern
Illinois University, Carbondale, IL, USA
Massimo Donelli Department of Information Engineering and Computer Science,
Polo Scientifico e Tecnologico Fabio Ferrari, University of Trento, Trento, Italy
Reza Ehsani Citrus Research and Education Center/IFAS, University of Florida,
Lake Alfred, FL, USA
D. S. Jayas Department of Biosystems Engineering, University of Manitoba,
­Winnipeg, MB, Canada
Hemantha Jayasuriya Department of Soils, Water and Agricultural Engineering,
College of Agricultural and Marine Sciences, Sultan Qaboos University, Al Khoud,
Sultanate of Oman
C. Karunakaran Canadian Light Source Inc., University of Saskatchewan,
­Saskatoon, SK, Canada
Lav R. Khot Department of Biological Systems Engineering, Washington State
University, Pullman, WA, USA
A. Manickavasagan Department of Soils, Water and Agricultural Engineering,
College of Agricultural and Marine Sciences, Sultan Qaboos University, Al Khoud,
Sultanate of Oman
Sindhuja Sankaran Department of Biological Systems Engineering, Washington
State University, Pullman, WA, USA
Preetam Sarkar Department of Food Process Engineering, National Institute of
Technology, Rourkela, Orissa, India
M. Teena Department of Soils, Water and Agricultural Engineering, College of Agricultural and Marine Sciences, Sultan Qaboos University, Al Khoud, Sultanate of Oman
xi
xii
Contributors
Gabriel Thomas Department of Electrical and Computer Engineering, University
of Manitoba, Winnipeg, MB, Canada
Neetha Udayakumar School of Biosystems Engineering, University College
Dublin, Belfield, Dublin, Ireland
N. Yasasvy Department of Electrical and Computer Engineering, Southern Illinois
University, Carbondale, IL, USA
About the Editors
Dr. Annamalai Manickavasagan, Ph.D., PEng
(Canada) obtained Ph.D. from the University of
Manitoba, Canada. He is a licensed professional
engineer (PEng) in the province of New Brunswick,
Canada. After Ph.D., he worked with McCain Foods
Limited (Canada) as Scientist. At present, he is
working as an Assistant Professor at the College of
Agricultural and Marine Sciences, Sultan Qaboos
University, Oman. He has published 2 books, 5
book chapters, and more than 60 scientific papers in
peer reviewed journals and international conferences. He has diversified research
and management experience with academic institutions and industries in Canada,
Malaysia, India, and Oman.
Dr. Hemantha Jayasuriya, Ph.D., CEng obtained
B.Sc. Eng. (Hons) in Mechanical Engineering in
1983. His M.Eng. and Ph.D. in Agricultural
Engineering were from AIT Thailand. He is a
chartered mechanical engineer by profession with
over 7 years of industrial experience and over 20
years of academic experience in teaching, research,
and administrative activities during his career. He
has supervised and completed nearly 40 graduate
students including several Ph.D. students and
published over 80 scientific publications in peer
reviewed journal, and international conference
proceedings. He has also published technical
reports and book chapters and possesses patents. He has membership credentials
with international professional bodies such as ASABE, ISPA, ISTVS, and AAAE
and currently the Vice Presidents of AAAE for Energy, Environment and emerging
Technologies. Since August 2009, he is a Faculty Member at the Department of
Soils, Water and Agricultural Engineering, College of Agricultural and Marine
Sciences, Sultan Qaboos University, Oman.
xiii
Chapter 1
Introduction to the Electromagnetic
Spectrum
Sindhuja Sankaran and Reza Ehsani
Introduction
Electromagnetic radiation is a form of energy released and absorbed by charged
particles. This radiation has specific electrical and magnetic properties. The wavelength range corresponding to the electromagnetic radiation is termed the ‘electromagnetic spectrum.’ The way in which the electromagnetic spectrum interacts with
any material can be used in qualitative and quantitative analysis of various materials.
Therefore, the electromagnetic spectrum is often used to assess various physical and
chemical properties of objects in food and agriculture.
The electromagnetic spectrum is defined by three basic factors. These are frequency
(f), wavelength (λ), and photon energy (E). The number of wave cycles per unit time
is called frequency (Hz, number of cycles per second). The wavelength is inversely
proportional to the frequency. The relationship between frequency, wavelength, and
energy is given by the following equations:
f =
c
Λ
(1.1)
f =
E
h
(1.2)
E=
hc
Λ
(1.3)
S. Sankaran (*)
Department of Biological Systems Engineering, Washington State University,
LJ Smith 202, PO Box 64120, Pullman, WA 99164, USA
e-mail: sindhuja.sankaran@wsu.edu
R. Ehsani
Citrus Research and Education Center/IFAS, University of Florida,
700 Experiment Station Road, Lake Alfred, FL 33850, USA
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_1, © Springer-Verlag Berlin Heidelberg 2014
1
S. Sankaran and R. Ehsani
2
Table 1.1 Electromagnetic spectrum with specific wavelength and frequency ranges
Wavelength
Low
|
|
|
|
|
High
Frequency
High
|
|
|
|
|
Low
Range*
Wavelength (m)
Frequency (Hz)
Gamma radiation
X-ray radiation
Ultraviolet radiation
Visible radiation
Infrared radiation
Microwave radiation
Radio waves
<10−11
>3 × 1019
3 × 1017–3 × 1019
7.5 × 1014–3 × 1017
4.3 × 1014–7.5 × 1014
3 × 1012–4.3 × 1014
3 × 109–3 × 1012
<3 × 109
10−9–10−11
4 × 10−7–10−9
7 × 10−7–4 × 10−7
1 × 10−5–7 × 10−7
0.01–1 × 10−5
>0.01
*Note The boundaries between groups are not well-defined
1
2
3
4
5
6
1. Transmission
2. Refraction
3. Diffusion
4. Absorption
7
5. Emission
6. Specular reflection
7. Diffuse reflection
Fig. 1.1 Possible interactions between object and electromagnetic spectra
where c refers to the speed of light in vacuum (299,792,458 m/s) and h refers to
Planck’s constant (6.62606957 × 10−34 J s). Depending on the wavelength range
and frequency, the electromagnetic spectrum is broadly classified into different
groups (Table 1.1).
The wavelength is generally represented in nm, but sometimes (infrared and
higher) are represented as the wavenumber. The wavenumber is the reciprocal of
wavelength and is usually represented as cm−1.
Wavenumber(cm−1 ) =
107
(nm)
(1.4)
Electromagnetic radiation is used in different types of spectroscopic techniques.
Different types of interactions can be used for studying the properties of materials using spectroscopic techniques. The interactions (Fig. 1.1) commonly used in
food and agriculture application are: (a) absorption, where electromagnetic radiation is absorbed by the object (e.g., photosynthesis); (b) transmission, where the
objects allow the passage of electromagnetic radiations (e.g., light passing through
window panel); (c) reflectance, where radiation is bounced back in one or many
directions (e.g., mirror); and (d) emission, where the objects emit electromagnetic
radiation resulting from the transition of energy state (e.g., fluorescence). In addition, vibrations can also be utilized for studying the object properties.
1
Introduction to the Electromagnetic Spectrum
3
Instrumentation Used for Imaging
The imaging system used in any spectroscopic technique requires few basic
instrumentation components. Some of the key components are source of light/
radiation, optical components, detector, data acquisition system, and a computer.
The components in instrumentation may vary with spectroscopic technique and
type of application. In food applications, most of the imaging is performed in controlled environment conditions; however, in agriculture, imaging can be performed
in controlled environment or field conditions depending on the application. The
components of the instrumentation can be different depending on the spectroscopic imaging technique used for a specific application. For example, beam splitters are an integral part of mid-infrared and terahertz spectroscopic techniques.
In near-infrared imaging system to study a food product or plant in controlled environmental conditions, the first step in system development is the fabrication of a set-up
or frame that will prevent the extraneous light from entering the imaging system. The
frame is usually made from metal and painted black inside to prevent reflection. It also
incorporates an opening side to position the samples. The dimension of the system
will depend on size of the object to be imaged and other related system components.
The light source should be such that it provides radiation in the near-infrared range
(e.g., halogen lamp). A monochromator or diffuser can be used to control the radiation
from the light source. The near-infrared camera has the required optics to capture the
reflectance image from the object in one, multiple, or numerous wavelength regions
with a specific bandwidth. For the development of 3D images, more cameras may be
used at different angles or the object can be rotated using mechanical components.
The image data from the camera is acquired using computer or similar systems, which can be post-processed or processed real time. A critical step in image
acquisition both in controlled environment and field conditions is radiometric calibration. It is very important to perform calibration to correct the instrument for
existing light intensity and nonlinear sensitivity of the detectors with respect to
wavelength. The instrumentation for different spectroscopic imaging systems such
as infrared imaging, fluorescence imaging, and thermal imaging can be found
in literature (Lu and Chen 1999; Kim et al. 2001; Chen et al. 2002; Wang and
Paliwal 2007; Vadivambal and Jayas 2011).
Methodologies and Techniques
Spectroscopic and imaging data processing and analysis are an important part of
data collection and interpretation. The spectral data or images should be preprocessing to eliminate the presence of systematic and non-systematic errors or noises.
Depending on the spectral methods, one or more spectral processing procedures
can be performed (Fig. 1.2). Some of the commonly used spectral processing
methods include baseline correction, normalization or scaling, smoothing, and differentiation (estimating first and second derivatives)/transformation. The baseline
4
S. Sankaran and R. Ehsani
Fig. 1.2 Electromagnetic spectral data processing and analysis
correction is performed to remove background variation in the data. Although
there are several ways to perform baseline correction, the simplest method for
removing the slope from the baseline is by drawing a linear fit across the spectra
such that the intercept and slope is zero. Similarly, normalization is a form of signal processing where the noise is removed and spectral signals are scaled.
Smoothing is simplest form of noise removal. The methods of simple smoothing
include moving average (averaging few data points to remove noise), least square fitting with a first- or second-degree polynomial, local regression fitting, and SavitzkyGolay filtering. Another form of improving the spectral signals is using derivatives. The
derivatives can enhance spectral resolution (especially when the changes are minor)
and allow spectral features extraction and quantitative analysis. Other advanced form
of smoothing could be the application of Fourier Transform and wavelet-based smoothing. In addition to these methods, images can further be processed using several techniques. The noise can be removed by low-pass, high-pass, mean-pass, or median-pass
filtering. During image resampling, the number of pixels can be increased or decreased
based on the requirements. Contrast enhancement is possible through histogram scaling, image equalization, linear contrast stretch, density slicing, and image matching.
Thresholding and segmentation can also be performed to further process the data.
The spectroscopic and imaging techniques generate a large quantity of data.
There are several methods to reduce the number of spectral features. The methods such as principal component analysis (PCA), cluster analysis, singular value
decomposition (SVD), partial least squares (PLS), wavelet decomposition, or feature extraction from the processed signals can reduce the large amount of data.
The extracted features can be ratios such as vegetation indices in the visible–nearinfrared spectra, peak features such as peak location, height, width, area under the
curve, or wavebands representing a specific condition. These wavebands can be
selected by methods such as forward/backward feature selection, PLS regression,
PLS discriminant analysis, principal component regression, canonical analysis,
and multi-linear regression. In images, in addition to the above-mentioned feature
extraction/selection, size, shape, color, uniformity, contrast, correlation, homogeneity, and texture features can also be derived. Thus, a set of critical spectral features can be selected from large dataset to improve the data analysis efficiency.
The multivariate data, either in the form of spectral reflectance values or spectral features, are analyzed for qualitative and quantitative analysis. The qualitative
1
Introduction to the Electromagnetic Spectrum
5
Fig. 1.3 Applications of spectral regions of the electromagnetic spectra
analysis involves classification, where groups or categories of features are identified, for example, classification of healthy from diseased plants, classification of
non-adulterated from adulterated food. Classification can have two or more groups
or classes. Spectral quantitative analysis involves prediction. During prediction,
specific properties of the object are estimated. Estimating nutrient concentration in
a leaf and predicting total soluble solids in juice are a few examples of prediction
studies. Supervised and unsupervised machine learning techniques are used to further process the data into interpretable results. These supervised techniques can be
simple techniques such as Naïve-Bayes classifier, discriminant analysis, k-nearest
neighbor, or more complex techniques such as fuzzy logic, decision trees, support
vector machine (SVM), and artificial neural networks (ANN).
Imaging Applications in Agricultural and Food Production
Systems
The different ranges of the electromagnetic spectra have several applications in food
and agriculture (Fig. 1.3). This section will summarize brief theory and applications.
Gamma-ray Imaging
The gamma rays have very high frequency, low wavelength, and high energy. The
gamma rays are emitted from radioisotopes such as cobalt-60 and cesium-38 (Farkas
2006; Osterholm and Norgan 2004). These radiations are ionizing and have high
6
S. Sankaran and R. Ehsani
energy for penetration capable for effective irradiation. One of the applications of
gamma-ray imaging is in assessing soil properties (Pires et al. 2002, 2004).
In food, they are commonly used for irradiation of food materials to prevent
food spoilage and contamination, and decrease infestation (killing and sterilizing insects). In addition to these applications, they are also used in processes
to improve food quality such as sprout inhibition, decrease ripening, and delaying senescence in some fruits and vegetation to enhance the shelf-life of the food
products.
Another possible application of gamma radiations in agriculture is to induce
mutation through gamma exposure for improving plant genetics through breeding, also termed as radiation breeding. During the early years, about 64 % of the
mutants were developed using gamma rays, while X-rays accounted to 22 % of
radiation breeding (Ahloowalia et al. 2004). There have been few studies that
applied gamma radiation to improve plant quality. Rice mutants have frequently
been studied compared with other crops. The short height varieties of rice induced
by gamma radiations have been developed both in India and the USA. In China,
mutant variety ‘Zhefu’ was developed that provided short growth period, high
yield, and disease resistance to rice blast. Similarly, mutations using gamma rays
on barley, soybean, peas, cotton, poppies, and pear crops have also been studied
(Ahloowalia et al. 2004).
X-ray Imaging
X-ray technologies, similar to gamma rays, have high frequency and energy, and are
often used for irradiation and plant breeding applications. In addition to irradiation
and plant breeding, X-ray computer tomography (CT) imaging is used for several
applications. One such application is to monitor food quality, even food products
such as grains, nuts, seeds, and hard-shelled fruits. These food products can be
monitored for size, density, and pests. Barcelon et al. (1999) scanned fresh and ripe
peaches using X-ray computed tomographic scanner to determine the relationship
between the physico-chemical properties such as moisture, density, soluble solids,
acidity, and pH with CT number in the images. The study found that the CT number
could be related to all of these features either directly or indirectly. The CT number
increased with increase in density, moisture, and acidity with R2 higher than 0.86.
The highest correlation (R2 = 0.9903) was found between density and CT number.
Similarly, the CT values were inversely proportional to soluble solids and pH values
(R2 = 0.89). Other examples of X-ray radiography applications include: detection of
defects in apples (Schatzki et al. 1997; Kim and Schatzki 2000), bone detection in
chicken and fish (Jamieson 2002), detecting split pits in peaches (Han et al. 1992),
pinhole damage in almonds (Kim and Schatzki 2001), and internal defects in sweet
onion (Tollner et al. 1999). Brosnan and Sun (2004) reported that the development
of machine vision techniques is important as it will satisfy the high demands and
requirements of the food industry.
1
Introduction to the Electromagnetic Spectrum
7
Another unique application in food and agriculture is X-ray fluorescence (XRF)
spectroscopy. In XRF spectroscopy, the secondary/fluorescent X-ray emission
from an object which has been activated using X-ray or gamma rays is observed.
This technology is used for food quality/composition monitoring, although other
applications such as cotton fiber maturity determination have been found (Wartelle
et al. 1995). The total reflection XRF can be used for multi-elemental analysis of food products. The benefits of total reflection XRF are as follows: rapid,
non-destructive sensing, low detection limits (even as low as the ng or pg level),
reliable for multi-elemental analysis with single-element internal standard, not
requiring sample preparation using chemicals, low cross-contamination, and better
accuracy (Xie et al. 1998; Golob et al. 2005). This is especially important to prevent micronutrient contamination that may have adverse effect on health. Golob
et al. (2005) detected 16 different trace and minor elements using total reflection
XRF on eight different types of honey, broadly classified into nectar, nectar and
honeydew (Chestnut), and honeydew categories. The study found that chestnut
honey had high concentration of rubidium and calcium, while honeydew honey
had high concentrations of sulfur, chlorine, potassium, and rubidium. The XRF
has also been used to estimate total sulfur concentration to indirectly determine
glucosinolate content in rapeseed meal (Schnug and Haneklaus 1988).
Ultraviolet Imaging
The ultraviolet (UV) region has wavelengths lower than that of visible light.
Sunlight can be natural source of UV, while artificial source can come from mercury, argon, and deuterium lamps. Much of UV light is non-ionizing in nature.
Broadly, the UV radiation is classified as UV-A (315–400 nm), UV-B (280–
315 nm), and UV-C (100–280 nm). While UV-A and UV-B can be harmful to
human health, a part of the UV-C spectrum is used for germicidal applications
(200–280 nm).
Fluorescence spectroscopic imaging has found several applications in food and
agriculture. Fluorescence is a type of emission from an object that is excited by
either UV or visible-infrared electromagnetic radiation. The advantages of fluorescence spectroscopy are the rapidness and non-invasiveness. Blasco et al. (2007)
found that a UV-based computer vision system was effective in identifying stemend injuries in citrus fruits, which was used for fruit sorting. Similarly, Slaughter
et al. (2008) used UV-fluorescence as a non-contact technique for detecting freezedamaged oranges.
In food and agriculture, the most practical application is utilizing fluorescence
imaging for stress detection. There are two types of fluorescence emissions in
plants. The fluorescence from the leaf epidermis and fluorophores such as flavonoids, phenolics, NADH, and others which are present in leaf veins are termed
as ‘blue-green fluorescence.’ The fluorescence from the plant pigments such as
chlorophyll is called ‘chlorophyll fluorescence.’ The fluorescence output from the
8
S. Sankaran and R. Ehsani
leaf epidermis in the blue and green regions of the spectrum upon UV excitation
(natural or artificial light) is due to the presence of cinnamic acids such as ferulic
acid (Malenovský et al. 2009). This fluorescence can be used in identifying nitrogen- and water-stressed plants (Apostol et al. 2003). The cinnamic acids and other
compounds present in the fruits can also be measured using fluorescence. The cinnamic acids such as p-coumaroyl-glucose and cinnamoyl-glucose absorb and emit
light in UV range (Wulf et al. 2008).
Similar to fluorescence spectroscopic imaging, there is another atomic emission technique called as laser-induced fluorescence (LIF), where a high-energy
laser source is used to excite the atoms or molecules, which emit at a longer wavelength that is detected using a sensor. The UV light can be used as a laser source.
Both UV-induced fluorescence and laser-induced fluorescence spectroscopy can be
applied in both food and agriculture.
The germicidal property of UV radiation makes it relevant for application in
food disinfection. The UV-C can be used to disinfect materials used for food packaging and to disinfect water used as an ingredient. Other possible applications of
UV are detection of chemical residues such as alfatoxines and microorganisms
such as coliforms (Bintsis et al. 2000).
Near-infrared Imaging and Spectroscopy
Hyperspectral imaging techniques are most widely used optical sensing technique.
Visible and near-infrared imaging offers a rapid, non-destructive, and cost-effective method for several food and agricultural applications.
The spectroscopic and imaging studies have been conducted on the detection of abiotic and biotic stress in plants (Sankaran et al. 2010a; Polischuk et al.
1997; Spinelli et al. 2006; Naidu et al. 2009). The visible and infrared regions of
the electromagnetic spectra are known to provide critical information on the physiological status in plants (Muhammed 2002, 2005; Xu et al. 2007), and thus, some
of the spectral signatures that a specific to a stress conditions can be used to detect
plant diseases (West et al. 2003), even in asymptomatic stages. In general, visible
and infrared spectroscopy is used together for stress detection in plants (Malthus and
Madeira 1993; Bravo et al. 2003; Huang et al. 2004; Larsolle and Muhammed 2007).
Hyperspectral imaging is often used for monitoring the food quality (Kim et al.
2001, 2002; Mehl et al. 2004; Yao et al. 2005; Tallada et al. 2006; Gowen et al.
2007; Mahesh et al. 2008; Sighicelli et al. 2009). Multispectral images of citrus
fruits have been used to evaluate fruit quality to develop a machine vision system
(Aleixos et al. 2002). The hyperspectral imaging applications for monitoring food
quality and safety have been reviewed (Gowen et al. 2007). The review article discusses the system development, image processing techniques, and various applications. The detection of bruises in apples has been studied using hyperspectral
imaging (Lu 2003; Xing and Baerdemaeker 2005; Xing et al. 2005; Nicolai et al.
2006; ElMasry et al. 2008). The spectral range between 1,000 nm and 1,340 nm
1
Introduction to the Electromagnetic Spectrum
9
was found to be suitable for apple bruise detection (Lu 2003). Similar studies
using hyperspectral imaging by Xing et al. (2005) and ElMasry et al. (2008) found
that the range of 558–960 nm could be used to identify apple bruises.
Mid-infrared and Terahertz Imaging and Spectroscopy
The unique property of mid-infrared region is the ability to detect biochemical
­compounds such as sugars and acids in leaves and in other materials such as corn, jellies, and food supplements (Dupuy et al. 1997; Mascarenhas et al. 2000; Kačuráková
and Wilson 2001; Sankaran et al. 2010a, b). Winson and Tapp (1999) reviewed different techniques on mid-infrared spectroscopy for food analysis. Agricultural applications include the qualitative and quantitative analysis of agricultural soils (Reeves
et al. 2001; McCarty et al. 2002; Janik et al. 2007). With the developments and
advancements in Fourier transform infrared (FTIR) technology, the application of
mid-infrared region further expanded in the field of food and agriculture.
The FTIR technology has been used for a number of application such as studying/detection of food-borne pathogens (Burgula et al. 2007; Alvarez-Ordóñez et al.
2011), food analysis (Wilson and Tapp 1999; VandeVoort 1992), coffee identification (Downey et al. 1997), detecting adulteration in oils (Lai et al. 1994; Marigheto
et al. 1998; Gurdeniz and Ozen 2009), and shelf-life studies (Cattaneo et al. 2005).
In addition to the soil analysis in agriculture, mid-infrared spectroscopy has
also been applied for plant stress detection (Sankaran et al. 2010a, b; Hawkins
et al. 2010a, b). Sankaran et al. (2010a) used a simple, rugged, portable mid-infrared spectrometer for detection citrus disease (Huanglongbing, HLB) in leaves. The
study found that the starch accumulation, a typical physiological change associated with HLB, could be identified using mid-infrared spectroscopy in HLBinfected leaves.
Over the years, terahertz (THz) frequencies (0.1–10 THz) are being applied to a
greater extent. The terahertz technology is applicable for measuring water content
in leaves and food products. The water stress in crops can be identified using terahertz frequency, as the water molecules absorb this spectral range to a great extent
(Hadjiloucas et al. 2009). Food applications can range from detecting moisture content to detection of antibiotics and pesticides (Gowen et al. 2010). Similarly, Jansen
et al. (2010) discusses multiple types of terahertz systems and their applications in
monitoring food quality and plant breeding, in addition to industrial applications.
Thermal Imaging
The thermal infrared region ranges from 3 to 14 µm. In infrared thermography
or imaging, the infrared thermal energy is utilized to acquire thermal variations
and convert the thermal spectral reflectance into a visible image. Thermal infrared
10
S. Sankaran and R. Ehsani
imaging makes non-contact detection of surface temperatures possible (Gowen
et al. 2010). Thermal imaging techniques have also been utilized for food and agricultural applications. Different types of thermography, imaging systems, processing methods, and applications in food have been explained by Gowen et al. (2010).
Some of the food applications are foreign body detection, grain quality, post-harvest quality, and food quality monitoring (Bulanon et al. 2008; Gowen et al. 2010).
In agriculture, infrared thermography can be used for non-invasive detection
of plant stress (Chaerle et al. 1999, 2001). The thermal imaging is often used to
assess abiotic stress conditions such as water stress in crop, monitoring irrigation,
and developing irrigation regimes (Wang et al. 2010; Gonzalez-Dugo et al. 2012;
Ballester et al. 2013). Other abiotic stress detection include study of ice nucleation and freezing of plants (Fuller and Wisniewski 1998). However, applications
on crop disease detection have also been found. Lenthe et al. (2007) used infrared
thermography to establish a relationship between the leaf microclimate and fungal
diseases in wheat fields. Although the infected leaf area could not be identified,
infrared thermography was able to predict the microclimate.
Microwave Imaging
The microwave is another non-visible spectrum used in imaging applications. The
microwave frequency causes dielectric heating through energy absorption in water.
For this reason, it is commonly used in microwave ovens and disinfection applications. The microwave imaging, also called as radar tomography, has been used to
evaluate physical properties of food. Abdullah et al. (2004) used microwave-based
imaging to assess moisture content in oil palm grain. The imaging technique was
able to identify the homogeneity and heterogeneity in moisture content, ranging
from 12 to 39 % in the sample. Similarly, Huisman et al. (2003) used the imaging technique to evaluate moisture content in soil. Microwave technology is commonly used in food processing (Schiffmann 1986; Datta 2001; Vadivambal and
Jayas 2010) and sample preparation in agriculture (Hawkins et al. 2010a, b).
Radio Wave Imaging
The magnetic resonance imaging (MRI) technique utilizes the magnetic field and
pulses of radio wave radiation energy to evaluate properties of objects, mostly
applied for diagnosis of various ailments internal to human and animal bodies. In
MRI, the atomic nuclei of the object is magnetized using strong magnets and the
nuclei rotates the magnetic field at variable speeds, which can be detected by the
scanner and converted into usable data through Fourier Transform. The hydrogen
atom is used as a target atom in the MRI technique because water is abundant in
all biological systems.
1
Introduction to the Electromagnetic Spectrum
11
The magnetic resonance imaging technique has been used for pre- and post-harvest
studies in fruits and vegetables (Clark et al. 1997), food quality evaluation (Du and
Sun 2004), and texture analysis (Thybo et al. 2004a), bruising (Thybo et al. 2004b),
and formation of ice during freezing (Kerr et al. 1998) in potatoes.
Conclusions
In recent years, the applications of spectroscopic techniques have been vastly
increasing in the field of food and agriculture. The advancements in optics, the
availability of several affordable sensor systems, and the broad exposure of its
potential applications have made this possible. The major benefits of the optical spectroscopic techniques over chemical, biochemical, or molecular analysis
for food and agricultural applications are speed, minimal to no sample preparation, multiple attributes can be measured simultaneously, and accurate detection.
Researchers are further exploring the potential applications of different regions of
electromagnetic spectra. Similarly, industries are utilizing electromagnetic sensor
technology for rapid monitoring of products. Several applications in the field of
food and agriculture are further discussed in individual chapters.
References
Abdullah MZ, Guan LC, Lim KC, Karim AA (2004) The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. J Food Eng
61(1):125–135
Ahloowalia B, Maluszynski M, Nichterlein K (2004) Global impact of mutation-derived varieties. Euphytica 135:187–204
Aleixos N, Blasco J, Navarron F, Molto E (2002) Multispectral inspection of citrus in real-time
using machine vision and digital signal processors. Comput Electron Agric 33:121–137
Alvarez-Ordonez A, Mouwen D, Lopez M, Prieto M (2011) Fourier transform infrared spectroscopy as a tool to characterize molecular composition and stress response in foodborne pathogenic bacteria. J Microbiol Methods 84:369–378
Apostol S, Viau A, Tremblay N, Briantais J, Prasher S, Parent L, Moya I (2003) Laser-induced
fluorescence signatures as a tool for remote monitoring of water and nitrogen stresses in
plants. Can J Remote Sens 29:57–65
Ballester C, Jimenez-Bello M, Castel J, Intrigliolo D (2013) Usefulness of thermography for
plant water stress detection in citrus and persimmon trees. Agric For Meteorol 168:120–129
Barcelon E, Tojo S, Watanabe K (1999) X-ray computed tomography for internal quality evaluation of peaches. J Agric Eng Res 73:323–330
Bintsis T, Litopoulou-Tzanetaki E, Robinson R (2000) Existing and potential applications of
ultraviolet light in the food industry—a critical review. J Sci Food Agric 80:637–645
Blasco J, Aleixos N, Gomez J, Molto E (2007) Citrus sorting by identification of the most common defects using multispectral computer vision. J Food Eng 83:384–393
Bravo C, Moshou D, West J, McCartney A, Ramon H (2003) Early disease detection in wheat
fields using spectral reflectance. Biosyst Eng 84:137–145
Brosnan T, Sun D (2004) Improving quality inspection of food products by computer vision—a
review. J Food Eng 61:3–16
12
S. Sankaran and R. Ehsani
Bulanon D, Burks T, Alchanatis V (2008) Study on temporal variation in citrus canopy using
thermal imaging for citrus fruit detection. Biosyst Eng 101:161–171
Burgula Y, Khali D, Kim S, Krishnan S, Cousin M, Gore J, Reuhs B, Mauer L (2007) Review of
mid-infrared Fourier transform-infrared spectroscopy applications for bacterial detection. J
Rapid Methods Autom Microbiol 15:146–175
Cattaneo T, Giardina C, Sinelli N, Riva M, Giangiacomo R (2005) Application of FT-NIR and
FT-IR spectroscopy to study the shelf-life of Crescenza cheese. Int Dairy J 15:693–700
Chaerle L, De Boever F, Van Montagu M, Van der Straeten D (2001) Thermographic visualization of cell death in tobacco and Arabidopsis. Plant Cell Environ 24:15–25
Chaerle L, Van Caeneghem W, Messens E, Lambers H, Van Montagu M, Van Der Straeten D
(1999) Presymptomatic visualization of plant-virus interactions by thermography. Nat
Biotechnol 17:813–816
Chen YR, Chao K, Kim MS (2002) Machine vision technology for agricultural applications.
Comput Electron Agric 36(2):173–191
Clark CJ, Hockings PD, Joyce DC, Mazucco RA (1997) Application of magnetic resonance imaging to pre-and post-harvest studies of fruits and vegetables. Postharvest Biol Technol 11(1):1–21
Datta AK (2001) Handbook of microwave technology for food application. CRC Press, Florida
Downey G, Briandet R, Wilson R, Kemsley E (1997) Near-and mid-infrared spectroscopies in
food authentication: coffee varietal identification. J Agric Food Chem 45:4357–4361
Du CJ, Sun DW (2004) Recent developments in the applications of image processing techniques
for food quality evaluation. Trends Food Sci Technol 15(5):230–249
Dupuy N, Wojciechowski C, Ta C, Huvenne J, Legrand P (1997) Mid-infrared spectroscopy and
chemometrics in corn starch classification. J Mol Struct 410:551–554
ElMasry G, Wang N, Vigneault C, Qiao J, ElSayed A (2008) Early detection of apple bruises on
different background colors using hyperspectral imaging. Lwt-Food Sci Technol 41:337–345
Farkas J (2006) Irradiation for better foods. Trends Food Sci Technol 17:148–152
Fuller M, Wisniewski M (1998) The use of infrared thermal imaging in the study of ice nucleation and freezing of plants. J Therm Biol 23:81–89
Golob T, Dobersek U, Kump P, Necemer M (2005) Determination of trace and minor elements in
Slovenian honey by total reflection X-ray fluorescence spectroscopy. Food Chem 91:593–600
Gonzalez-Dugo V, Zarco-Tejada P, Berni J, Suarez L, Goldhamer D, Fereres E (2012) Almond
tree canopy temperature reveals intra-crown variability that is water stress-dependent. Agric
For Meteorol 154:156–165
Gowen A, O’Donnell C, Cullen P, Downey G, Frias J (2007) Hyperspectral imaging—an emerging
process analytical tool for food quality and safety control. Trends Food Sci Technol 18:590–598
Gowen A, Tiwari B, Cullen P, McDonnell K, O’Donnell C (2010) Applications of thermal imaging in food quality and safety assessment. Trends Food Sci Technol 21:190–200
Gurdeniz G, Ozen B (2009) Detection of adulteration of extra-virgin olive oil by chemometric
analysis of mid-infrared spectral data. Food Chem 116:519–525
Hadjiloucas S, Walker GC, Bowen JW, Becerra VM, Zafiropoulos A, Galvão RKH (2009) High
signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes. J Phys Conf Ser 183:012003.
doi:10.1088/1742-6596/183/1/012003 (ISSN 1742-6588)
Han Y, Bowers S, Dodd R (1992) Nondestructive detection of split-pit peaches. Transactions of
the ASAE 35:2063–2067
Hawkins SA, Park B, Poole GH, Gottwald TR, Windham WR, Albano J, Lawrence KC (2010a)
Comparison of FTIR spectra between Huanglongbing (Citrus greening) and other citrus maladies. J Agric Food Chem 58(10):6007–6010
Hawkins SA, Park B, Poole GH, Gottwald T, Windham WR, Lawrence KC (2010b) Detection of
Citrus huanglongbing by Fourier transform infrared–attenuated total reflection spectroscopy.
Appl Spectrosc 64(1):100–103
Huang MY, Huang WH, Liu LY, Huang YD, Wang JH, Zhao CH, Wan AM (2004) Spectral
reflectance feature of winter wheat single leaf infested with stripe rust and severity level
inversion. Trans Chin Soc Agric Eng 20:176–180
1
Introduction to the Electromagnetic Spectrum
13
Huisman JA, Hubbard SS, Redman JD, Annan AP (2003) Measuring soil water content with
ground penetrating radar. Vadose zone j 2(4):476–491
Jansen C, Wietzke S, Peters O, Scheller M, Vieweg N, Salhi M, Krumbholz N, Jördens C,
Hochrein T, Koch M (2010) Terahertz imaging: applications and perspectives. Appl Opt
49(19):E48–E57
Jamieson V (2002) Physics raises food standards. Phys World 15:21–22
Janik L, Merry R, Forrester S, Lanyon D, Rawson A (2007) Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Sci Soc Am J 71:507–514
Kacurakova M, Wilson R (2001) Developments in mid-infrared FT-IR spectroscopy of selected
carbohydrates. Carbohydr Polym 44:291–303
Kerr WL, Kauten RJ, McCarthy MJ, Reid DS (1998) Monitoring the formation of ice during
food freezing by magnetic resonance imaging. LWT-Food Sci Technol 31(3):215–220
Kim MS, Chen YR, Mehl PM (2001) Hyperspectral reflectance and fluorescence imaging system
for food quality and safety. Trans ASABE 44(3):721–730
Kim M, Lefcourt A, Chao K, Chen Y, Kim I, Chan D (2002) Multispectral detection of fecal
contamination on apples based on hyperspectral imagery: part I. Application of visible and
near-infrared reflectance imaging. Trans ASAE 45:2027–2037
Kim S, Schatzki T (2000) Apple watercore sorting system using X-ray imagery: I. Algorithm
development. Trans ASAE 43:1695–1702
Kim S, Schatzki T (2001) Detection of pinholes in almonds through X-ray imaging. Trans ASAE
44:997–1003
Lai Y, Kemsley E, Wilson R (1994) Potential of Fourier transform-infrared spectroscopy for the
authentication of vegetable-oils. J Agric Food Chem 42:1154–1159
Larsolle A, Muhammed H (2007) Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precis Agric
8:37–47
Lenthe J, Oerke E, Dehne H (2007) Digital infrared thermography for monitoring canopy health
of wheat. Precis Agric 8:15–26
Lu R (2003) Detection of bruises on apples using near-infrared hyperspectral imaging. Trans
ASAE 46:523–530
Lu R, Chen YR (1999) Hyperspectral imaging for safety inspection of food and agricultural
products. In photonics east (ISAM, VVDC, IEMB). International Society for Optics and
Photonics. pp 121–133
Mahesh S, Manickavasagan A, Jayas D, Paliwal J, White N (2008) Feasibility of near-infrared
hyperspectral imaging to differentiate Canadian wheat classes. Biosyst Eng 101:50–57
Malenovsky Z, Mishra K, Zemek F, Rascher U, Nedbal L (2009) Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. J Exp Bot
60:2987–3004
Malthus T, Madeira A (1993) High-resolution spectroradiometry—spectral reflectance of field
bean-leaves infected by Botrytis fabae. Remote Sens Environ 45:107–116
Marigheto N, Kemsley E, Defernez M, Wilson R (1998) A comparison of mid-infrared and
Raman spectroscopies for the authentication of edible oils. J Am Oil Chem Soc 75:987–992
Mascarenhas M, Dighton J, Arbuckle G (2000) Characterization of plant carbohydrates and
changes in leaf carbohydrate chemistry due to chemical and enzymatic degradation measured
by microscopic ATR FT-IR spectroscopy. Appl Spectrosc 54:681–686
McCarty G, Reeves J, Reeves V, Follett R, Kimble J (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66:640–646
Mehl P, Chen Y, Kim M, Chan D (2004) Development of hyperspectral imaging technique for the
detection of apple surface defects and contaminations. J Food Eng 61:67–81
Muhammed HH (2002) Using hyperspectral reflectance data for discrimination between healthy
and diseased plants, and determination of damage-level in diseased plants. In: IEEE proceedings of the 31st applied imagery pattern recognition workshop, pp 49–54
Muhammed H (2005) Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat. Biosyst Eng 91:9–20
14
S. Sankaran and R. Ehsani
Naidu R, Perry E, Pierce F, Mekuria T (2009) The potential of spectral reflectance technique for
the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars.
Comput Electron Agric 66:38–45
Nicolai B, Lotze E, Peirs A, Scheerlinck N, Theron K (2006) Non-destructive measurement of
bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol Technol 40:1–6
Osterholm M, Norgan A (2004) The role of irradiation in food safety. N Engl J Med
350:1898–1901
Pires LF, de Macedo JR, de Souza MD, Bacchi OO, Reichardt K (2002) Gamma-ray computed
tomography to characterize soil surface sealing. Appl Radiat Isot 57(3):375–380
Pires LF, Bacchi OOS, Reichardt K (2004) Damage to soil physical properties caused by soil
sampler devices as assessed by gamma ray computed tomography. Soil Res 42:857–863
Polischuk VP, Shadchina TM, Kompanetz TI, Budzanivskaya IG, Sozinov A (1997) Changes in
reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral
infection. Arch Phytopathol Plant Prot 31:115–119
Reeves J, McCarty G, Reeves V (2001) Mid-infrared diffuse reflectance spectroscopy for the
quantitative analysis of agricultural soils. J Agric Food Chem 49:766–772
Sankaran S, Ehsani R, Etxeberria E (2010a) Mid-infrared spectroscopy for detection of
Huanglongbing (greening) in Citrus leaves. Talanta 83:574–581
Sankaran S, Mishra A, Ehsani R, Davis C (2010b) A review of advanced techniques for detecting
plant diseases. Comput Electron Agric 72:1–13
Schatzki T, Haff R, Young R, Can I, Le L, Toyofuku N (1997) Defect detection in apples by
means of x-ray imaging. Trans ASAE 40:1407–1415
Schiffmann RF (1986) Food product development for microwave processing. Food Technol
40:94–98
Schnug E, Haneklaus S (1988) Theoretical principles for the indirect determination of the total
glucosinolate content in rapeseed and meal quantifying the sulfur concentration via X-rayfluorescence (X-RF method). J Sci Food Agric 45:243–254
Sighicelli M, Colao F, Lai A, Patsaeva S (2009) Monitoring post-harvest orange fruit disease by
fluorescence and reflectance hyperspectral imaging. ISHS Acta Hortic 817:277–284
Slaughter D, Obenland D, Thompson J, Arpaia M, Margosan D (2008) Non-destructive freeze
damage detection in oranges using machine vision and ultraviolet fluorescence. Postharvest
Biol Technol 48:341–346
Spinelli F, Noferini M, Costa G (2006) Near infrared spectroscopy (NIRs): perspective of fire
blight detection in asymptomatic plant material. Acta Hortic 704:87–90
Tallada JG, Nagata M, Kobayashi T (2006) Detection of bruises in strawberries by hyperspectral
Imaging. 2006 ASABE annual international meeting, Portland, OR
Thybo AK, Jespersen SN, Lærke PE, Stødkilde-Jørgensen HJ (2004a) Nondestructive detection
of internal bruise and spraing disease symptoms in potatoes using magnetic resonance imaging. Magn Reson Imaging 22(9):1311–1317
Thybo AK, Szczypiński PM, Karlsson AH, Dønstrup S, Stødkilde-Jørgensen HS, Andersen HJ (2004b)
Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw
potatoes in combination with different image analysis methods. J Food Eng 61(1):91–100
Tollner EW, Shahin MA, Maw BW, Gitaitis RD, Summer DR (1999) Classification of onions
based on internal defects using imaging processing and neural network techniques. 1999
ASAE annual international meeting, Toronto, CA
Vadivambal R, Jayas DS (2010) Non-uniform temperature distribution during microwave heating
of food materials—A review. Food Bioprocess Technol 3(2):161–171
Vadivambal R, Jayas DS (2011) Applications of thermal imaging in agriculture and food industry-a review. Food Bioprocess Technol 4(2):186–199
Vandevoort F (1992) Fourier-transform infrared-spectroscopy applied to food analysis. Food Res
Int 25:397–403
Wang W, Paliwal J (2007) Near-infrared spectroscopy and imaging in food quality and safety.
Sens Instrum Food Qual Saf 1(4):193–207
Wang X, Yang W, Wheaton A, Cooley N, Moran B (2010) Efficient registration of optical and IR
images for automatic plant water stress assessment. Comput Electron Agric 74:230–237
1
Introduction to the Electromagnetic Spectrum
15
Wartelle L, Bradow J, Hinojosa O, Pepperman A, Sassenrathcole G, Dastoor P (1995)
Quantitative cotton fiber maturity measurements by X-ray-fluorescence spectroscopy and
advanced fiber information-system. J Agric Food Chem 43:1219–1223
West J, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney H (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Annu Rev Phytopathol
41:593–614
Wilson R, Tapp H (1999) Mid-infrared spectroscopy for food analysis: recent new applications and
relevant developments in sample presentation methods. Trac-Trends Anal Chem 18:85–93
Wulf J, Ruhmann S, Rego I, Puhl I, Treutter D, Zude M (2008) Nondestructive application of
laser-induced fluorescence spectroscopy for quantitative analyses of phenolic compounds in
strawberry fruits (Fragaria x ananassa). J Agric Food Chem 56:2875–2882
Xie M, von Bohlen A, Klockenkamper R, Gunther XJK (1998) Multielement analysis of Chinese
tea (Camellia sinensis) by total-reflection X-ray fluorescence. Z Lebensm-Unters Forsch
Food Res Technol 207:31–38
Xing J, Bravo C, Jancsok P, Ramon H, De Baerdemaeker J (2005) Detecting bruises on ‘Golden
Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosyst Eng 90:27–36
Xing J, De Baerdemaeker J (2005) Bruise detection on ‘Jonagold’ apples using hyperspectral
imaging. Postharvest Biol Technol 37:152–162
Xu H, Ying Y, Fu X, Zhu S (2007) Near-infrared spectroscopy in detecting leaf miner damage on
tomato leaf. Biosyst Eng 96:447–454
Yao H, Hruska Z, DiCrispino K, Brabham K, Lewis D, Beach J, Brown RL, Cleveland TE (2005)
Differentiation of fungi using hyperspectral imagery for food inspection. 2005 ASAE annual
international meeting, Tampa, FL
Chapter 2
Gamma-Ray Imaging
A. Manickavasagan and N. Yasasvy
Introduction
The gamma rays are the region in the electromagnetic spectrum with the highest
energy and the shortest wavelength. It is difficult to observe the wave properties of
the gamma rays as their wavelength is extremely shorter. It can ionize the matter
while interacting with an object. Gamma rays may be generated by several mechanisms such as annihilation of antimatter and matter, accelerations of charged particles by strong magnetic fields, and radioactive decay of the nucleus of an atom
(Richards 2001). The gamma-ray source can be very compact and does not require
external power. Their high penetration property makes them one of the most
important imaging techniques for the internal properties of extremely thick object.
Gamma rays can be used to produce images in situations where the X-ray cannot
penetrate. The gamma-ray images can be made with X-ray film in light-tight packages (Fig. 2.1).
Terminologies
Radionuclide or radioactive nuclide or radioactive isotope or radioisotope is
an atom with unstable nucleus characterized by excess energy available to be
imparted as radiation.
A. Manickavasagan (*)
Department of Soils, Water and Agricultural Engineering, College of Agricultural
and Marine Sciences, Sultan Qaboos University, PO Box 34, Al Khoudh PC 123, Oman
e-mail: manick@squ.edu.om
N. Yasasvy
Department of Electrical and Computer Engineering, Southern Illinois University,
Carbondale, IL 62901, USA
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_2, © Springer-Verlag Berlin Heidelberg 2014
17
18
A. Manickavasagan and N. Yasasvy
Fig. 2.1 Gamma-ray
imaging with a radioactive
source (Reproduced
from Richards 2001 with
permission from SPIE)
Radioactive decay occurs when particles are emitted from the nucleus of an
­unstable atom. Alpha, beta, and gamma are the most common types of radiation
emitted from a radioactive material.
Alpha decay occurs when a nucleus emits an alpha particle. The alpha particle is
identical to a helium nucleus. Uranium-238 becomes thorium-234 via the process
of alpha decay.
Beta decay occurs when a beta particle is emitted from a nucleus. There are two
types of beta decay: beta minus (β−) and beta plus (β+). β− is a type of beta decay
that emits an electron while β+ happens in the case of a positron emission.
Gamma decay occurs when a nucleus drops to a lower energy state from a higher
energy state. Unlike alpha and beta decay, the chemical element does not change
and carries no charge. The resulting emission produces gamma rays.
Half-life of a radioactive material is the amount of time required for half of the
atoms to undergo radioactive decay.
Radioisotopes
Most of the radioisotopes used in agricultural applications have relatively short
half-lives, ranging from a few minutes to a few hours, and hence, the experiment
needs to take place close to where the isotopes are being produced. One of the
most common ways to produce these radioisotopes is using a cyclotron. Charged
particles are rapidly accelerated from the center and move away spirally until they
emerge from the cyclotron at very high speed.
Gamma Camera
Gamma camera is a device developed for medical diagnostics to capture emitted
gamma radiation from internal radioisotopes to create images. It is also known
as scintillation camera. The process of capturing images using gamma camera
2
Gamma-Ray Imaging
19
Fig. 2.2 The schematic
diagram of a PET scan
(Reproduced from Richards
2001 with permission from
SPIE)
is known as scintigraphy. The basic concept of this camera was designed and
­developed by Hal Anger and therefore is also referred to as Anger Camera. This
camera detects radiation from the entire field of view and capable of recording
dynamic as well as static images of the area of interest. Gamma camera consists of
several components such as detector, collimator, PM tubes, preamplifier, amplifier,
pulsed height analyzer (PHA), X–Y positioning circuit, and display or recording
device (Saha 2006). The detector, PM tubes, and amplifiers are housed in a unit
called the detector head.
The collimator acts as a filter that allows only the gamma rays traveling perpendicular to the plane of the collimator and blocks out all other rays. The septum is
usually made of lead to absorb the gamma radiation heading toward the crystal at
an oblique angle. The gamma rays are then detected by the camera head and relay
the energy and the location data of the interacting gamma rays to the computer.
Positron Emission Tomography
The positron emission tomography (PET) scan does not require any external
gamma-ray source. The special tracer molecules are ingested or injected into the
living tissue. The tracers are specially prepared compounds to contain one or more
radioactive atoms that spontaneously emit positrons. The positrons are antimatters:
positively charged electrons that rapidly colloid with electrons in the neighboring atoms. The collision results in the annihilation of both the positron and electron and in the creation of two gamma rays with energy of a positron or electron
(Richards 2001). The PET scanners detect gamma rays with a ring of gamma-ray
detectors placed around the subject (Fig. 2.2). The gamma-ray detectors, also
known as scintillation crystals, convert the gamma rays into visible light, which
is then detected by a high speed light detector. The computer analyzes the electric signals from the light detectors and generates the image. PET can be used to
obtain both 2-dimensional (2-D) and 3-dimensional (3-D) images depending on
the detector setup.
20
A. Manickavasagan and N. Yasasvy
Fig. 2.3 PETIS with the
plant sample in the middle
of two oppositely positioned
scintillators (Watanabe et al.
2009)
Positron-Emitting Tracer Imaging System
The positron-emitting tracer imaging system (PETIS) was developed for the purpose of using the theory of PET in plants. It is equipped with a planar type imaging apparatus and radioisotopes tracers such as 11C, 13N, 15O, 52Fe, 52Mn, 64Cu,
and 107Cd that produced by a cyclotron and provides 2-D images (Kawachi et al.
2007). It is one of the powerful techniques for conducting research on the distribution and translocation of water, photoassimilate, mineral nutrients, and environmental pollutants to plants. The PETIS detects two gamma rays produced by
positron-emitting nuclides with a scintillation camera and therefore enables us
to study the movement of elements in intact plants in real time (Tsukamoto et al.
2004). It is a more compact system and provides flexibility in the way the environment is controlled (Fig. 2.3).
Plant Tomographic Imaging System
The gamma-ray count rates provide the time behavior of the activity in the corresponding part of the plant. Whereas in the PETIS, much more details are obtained
in a planar camera that provides a complete 2-D projections. However, more
complex plant organs such as fruits and root systems demand 3-D information
for critical evaluation of the biological system. Streun et al. (2007) designed and
developed a 3-D system called plant tomographic imaging system (PlanTIS). This
system was equipped with two opposing detector blocks, which can be rotated in
a horizontal plane. The gantry is assembled on a table, and the plant can be placed
on the table with region of interest inside the hole located on the table.
2
Gamma-Ray Imaging
21
Table 2.1 Isotope-imaging modalities and applications in various crops
Modality Crop
Radioisotope Application/Study
PETIS
Soybean
18F
Rice
13N
Rice, Tomato
15O
Oilseed rape
107Cd
Barley
Hemp
52Fe
Tomato
13C
Rice
107Cd
Rice
15O
Barley
Cowpea
52Mn
Broad bean
11C
Rice
15O
Eggplant
11C
Barley
Wheat
Tomato
11C
Brassica oleracea
18F
PlanTIS
PET
PET/CT Fodder radish
11C
18F, 48V
11C
11C
11C
References
Uptake and transportation Kume et al. (1997)
of water
Ammonium uptake and
Kiyomiya et al. (2001)
nitrogen movement
Uptake and translocation Mori et al. (2000)
of water
Inhibiting cadmium uptake Nakamura et al. (2013)
by application of
glutathione
Translocation of Fe
Tsukamoto et al. (2009)
Kinetics of carbon during Kawachi et al. (2006)
photosynthesis
Suwa et al. (2008)
Evaluation of salt stress
and its effect on
photosynthesis
Fujimaki et al. (2010)
Quantitative analysis of
uptake and translocation
of Cd
Translocation of H2O under Kiyomiya et al. (2001)
different conditions
Translocation of Mn
Tsukamoto et al. (2006)
Furukawa et al. (2001)
Vanadium uptake and
its effect of water
translocation
Modeling of photoassimi- Matsuhashi et al. (2005)
late flow
Effect of 5-aminolevulinic Tsukamoto et al. (2004)
acid on translocation
of H2O
Photoassimilate flow in
Kikuchi et al. (2008)
fruit
Translocation of 11C in root Beer et al. (2010)
Translocation of 11C in root Streun et al. (2007)
Translocation of 11C in
Kawachi et al. (2007)
fruits
Converse et al. (2012)
Establish protocols for
standardization of scanning technique
Soil–plant interactions
Garbout et al. (2012)
Applications of Gamma-ray Imaging in Agriculture
The mechanism of growth and development of fruits, vegetables, and other plant
parts is beneficial to the researchers in many fields of plant science. Gamma-ray
imaging has been successfully used for the quantification of various compounds
22
A. Manickavasagan and N. Yasasvy
and mechanism within plants. Table 2.1 summarizes some of the applications of
isotope imaging in various plants.
Photoassimilate Translocation Within Plants
The import and distribution of dry matter inside the fruits yield useful information. The quantification of photoassimilation and photosynthate export on leaf is
essential in the study of structural maturation of leaves, carbon balance, mechanism of the sink-source transition, phloem loading, and unloading in leaves and so
on (Kawachi et al. 2005).
The plant produce assimilates by photosynthesis from light, CO2, and water.
Then, the assimilate is translocated to the sink parts of the plant via phloem (Suwa
et al. 2008).
Kikuchi et al. (2008) investigated the enlargement mechanism of eggplant fruit
using 11CO2 and PETIS to visualize photoassimilate translocation to and distribution in the fruit. The 11CO2 was fed to a leaf and monitored the translocation
of 11C-labeled photoassimilate into the fruit by PETIS. The 11C signal intensity
increased gradually in the fruit, and its distribution was non-uniform. The velocity
of photoassimilate translocation through the peduncle was estimated as 1.17 cm/
min. Sixty minutes after the start of 11CO2 feeding, the 11C activity of the fruit
began to increase, and by 120 min, it had reached about 8 % of feeding 11CO2
(Fig. 2.4). It took about 60 min for the first [11C] photoassimilate to reach the fruit.
It was also reported that PETIS may be a powerful tool for revealing the mechanism of fruit development and maturity.
Kawachi et al. (2005) developed a method to quantitate photosynthetic rate
constant within leaf using 11CO2 and PETIS. In this study, the time activity curves
of 11CO2 gas input and leaf response were fitted to an appropriate compartmental tracer kinetic model, which applied influx and efflux for photoassimilation and
photosynthate delivery rate constants, respectively. The summary of calculated
photosynthesis parameters is given in Table 2.2.
Matsuhashi et al. (2005) modeled photoassimilate flow in an intact broad bean
(vicia faba L) with the help of images obtained from the PETIS. The radioactive
11CO was fed to a leaf together with air containing an ambient concentration of
2
non-radioactive carrier CO2 gas. In this study, the average flow speeds and the
distribution ratios of photoassimilates in the respective nodes and internodes of
the observed stem were estimated by transfer function analysis.
Suwa et al. (2008) studied the effect of salinity on tomato plants on assimilate
production and carbon translocation. The PETIS analysis of 11C translocation indicated that carbon translocation to roots was inhibited under salt conditions without
a direct effect on leaf Na accumulation or osmotic stress.
A PET (3D) was used by Kawachi et al. (2007) to 11C labeled photoassimilate
translocation into fruits of tomato with carbon-11-labeled carbon dioxide. It was
reported that the usage of PET in plants is not only useful in investigating plant
2
Gamma-Ray Imaging
23
Fig. 2.4 Photoassimilate translocation in intact eggplant fruit: a visible image; b serial PETIS
images of translocation of [11C] photoassimilates (images were continuously acquired every 10 s
for 3 h; each image in this figure represents the integration of 72 serial images (Reproduced from
Kikuchi et al. 2008 with permission from Japanese Society for Horticultural Science)
Table 2.2 Estimated photosynthesis parameters of Cannabis sativa L. var. sativa (CBDA strain,
hemp) at four light conditions (Kawachi et al. 2005)
Light condition (250 µmol photon m−2 s−1)
0
70
150
250
k1 Photoassimilation rate constant (/min)
k3 Assimilate export rate constant (/min)
–
–
0.306
0.00983
0.709
0.0131
0.829
0.0132
physiology such as mechanism of fruit, growth under various physiological condition, but also in improving agricultural techniques such as improving cultivation
conditions to obtain the best harvest in terms of quality and quantity.
24
A. Manickavasagan and N. Yasasvy
Hirose et al. (2013) developed a real-time radioisotope imaging system (RRIS)
to study the kinetics of nutrient uptake and transfer of photosynthetic products in
plants. The capability of this system was determined through a test run by seedlings of rice plant and 35S-labeled sulfate. It was reported that the developed system was capable of photon counting images and photographic images of the test
plant (using commercially available RGB color cameras).
Water Uptake and Translocation Within Plants
Water plays an important role in plant physiology; however, the water behavior,
movement, and distribution within the plant has not been studied well due to lack
of tools (Nakanishi et al. 2001).
Mori et al. (2000) used PETIS to visualize 15O-water flow in tomato and rice
plant in light and darkness. The flow rate in the stem of tomato and the shoot of
rice at 500 µmol m−2 s−1 light intensity was 1.9 and 0.4 cm/min, respectively.
Nakanishi et al. (2001) compared two isotopes (15O and 18F) in a water uptake
study for soybean plant using PETIS. It was found that 18F-labeled water was
taken up much faster than 15O-labeled water probably due to fluorine was moved
in the form of 18F-ion.
5-Aminolevulinic acid (ALA) at low concentration increases the chlorophyll
biosynthesis, photosynthesis, cold stress tolerance, and salt tolerance in plant.
Tsukamoto et al. (2004) studied the effect of ALA on H15
2 O translocation from the
roots to the shoots of rice plant in real time by a PETIS. It was reported that when
the plant was treated with 10 µM ALA, the velocity of H15
2 O translocation from 2
to 12 min after absorption increased to 126, 137, and 140 % that of the control at
1.5, 2.5, and 3.5 h after ALA treatment, respectively.
Kiyomiya et al. (2001) studied the effect of light on H15
2 O flow in rice plant
O
flow
was
activated
8
min
after
plants
were exposed to
using PETIS. The H15
2
bright light (1,500 µmol m−2 s−1), and it was gradually slowed when the light
was removed and finally completely stopped after 12 min. Whereas, in the plants
exposed to low light (1,500 µmol m−2 s−1), the H15
2 O flow was activated more
slowly and a higher translocation was observed in the same low light at the end of
the next dark period.
Mineral Uptake and Translocation Within Plants
Furukawa et al. (2001) measured the distribution of vanadium-48 (48V) in a cowpea plant (whole plant) after 3, 6, and 20 h of V treatment using PETIS. After the
20 h treatment, V was detected at the up ground part of the plant. The effect of
V uptake on plant activity, 18F-labeled water uptake was analyzed using PETIS.
It was reported that when a cowpea plant was treated with V for 20 h before
2
Gamma-Ray Imaging
25
Fig. 2.5 The PETIS images to show the transport and accumulation of Cd in oilseed rape plants.
a Field of view (rectangle) of a representative PETIS experiment. b Time series of PETIS images
showing the 107Cd signal (0–36 h). Each image shown is a composite of 45 original images collected
every 4 min. All plants are exposed to 107Cd in the root medium; plants on the centre and right were
exposed additionally to glutathione (GSH) and oxidized form of glutathione (GSSG), respectively
(Reproduced from Nakamura et al. 2013 with permission from Oxford University Press)
18F-labeled
water uptake, the total amount of 18F-labeled water uptake was drastically decreased.
The real-time translocation of Mn in barley at various conditions was visualized using PETIS by Tsukamoto et al. (2006). In general, 52Mn first accumulated in the discrimination center (DC) at the basal portion of the shoot. The
Mn-deficient plant showed greater translocation of 52Mn from roots to shoots than
did Mn-sufficient plant.
Nakamura et al. (2013) investigated the effects of the reduced form of glutathione (GSH) applied to specific organs (source leaves, sink leaves, and roots)
on cadmium (Cd) distribution and behavior in the roots of oilseeds rape plant
(Brassica napus) cultured hydroponically using PETIS. The translocation ratio of
Cd from roots to shoots was significantly lower in plants that had root treatment of
GSH than in control plants (Fig. 2.5).
Fujimaki et al. (2010) characterized the absorption, transportation, and accumulation of cadmium from culture to spikelet in an intact rice plant using PETIS. The
107Cd was fed to the hydroponic culture solution, and the serial images of Cd distribution in the intact rice plant was taken at the vegetative stage and at the grain
filling stage every 4 min for 36 h. It was reported that the rates of Cd absorption
by the root were proportional to Cd concentration in the culture solution within the
tested range (0.05–100 nM). The radial transport from culture to the xylem in the
root tissue was completed in less than 10 min. The Cd moved up through the shoot
organs with velocities of few centimeters per hour at both stages which was slower
than the bulk flow at the xylem. It arrived at the panicle 7 h after feeding and accumulated there constantly. The nodes exhibited the most intensive Cd accumulation
in the shoot at both stages.
26
A. Manickavasagan and N. Yasasvy
Fig. 2.6 PETIS imaging setup for soybean: the left-hand side figure shows the test plant (soybean), and white line shows the field of view of the PETIS. The right-hand side shows the imaging of 64Cu by the PETIS (The color scale bar on left-hand side represents the intensity of 64Cu)
(Watanabe et al. 2009)
The translocation of iron in barley (Hordeum vulgare L. cv. Ehimehadaka) was
studied using PETIS by Tsukamoto et al. (2009). It was reported that Fe deficiency
caused enhanced uptake and translocations to shoots. In the dark, the translocation
of 52Fe to the youngest leaf was equivalent to or higher than that under light condition, while the translocation to the older leaves was decreased in both Fe-deficient
and Fe-sufficient barley.
Watanabe et al. (2009) used 64Cu as a tracer in the soybean plan for the transportation from root to the leaves (Fig. 2.6). It was mentioned that 64Cu could be a
useful tracer for the use in plant studies such as the distribution and translocation
of copper in intact plants using the PETIS.
Ishikawa et al. (2011) visualized and quantitatively analyzed the real-time Cd
dynamics from roots to grains in rice cultivars that differed in grain Cd concentrations using PETIS (Figs. 2.7, 2.8, 2.9). The low Cd-accumulating cultivars (japonica
type) showed rapid saturation curves, whereas the high Cd-accumulating cultivars
(indica types) were characterized by curves with a peak within 30 min after 107Cd
supplementation, and a subsequent steep decrease resulting in maintenance of lower
Cd concentration in their roots. It was also mentioned that high Cd-accumulating
cultivars were characterized by rapid and abundant transfer to the shoots from the
roots, a faster transport velocity of Cd to the panicle, and Cd accumulation at high
levels in their panicles, passing through the nodal portions of the stems.
Soil Analysis
Although many naturally occurring elements have radioactive isotopes, only
potassium (40K), the decay series of uranium (238U and 235U and their daughters)
and thorium (232Th and its daughters) have long shelf life and abundant in the
2
Gamma-Ray Imaging
27
Fig. 2.7 Uptake and transportation of 107Cd in the roots of rice cultivars (vegetative stage). a
Photograph of test plants. The large-dotted rectangle indicates the FOV of PETIS. Nipponbare,
Koshihikari, and Sasanishiki are of the japonica type, showing low Cd-accumulating cultivars.
Choko-koku, Jarjan, and Anjana Dhan are of the indica type, showing high Cd-accumulating cultivars. b Serial images of Cd movement (0–36 h). c Time courses of Cd amounts in the roots surrounded by red lines in the black and white photograph. d Time course of Cd amounts in culture
solution surrounded by red line. Cd in the roots (pmol) and Cd in solution (pmol) indicate the
sums of radioactive 107Cd and non-radioactive Cd (Ishikawa et al. 2011)
environment and produce gamma rays of energy and intensity to be measured by
gamma-ray spectrometry (Rossel et al. 2007).
The current methods for soil sampling and lab analysis for soil sensing are
­time-consuming and expensive. Rossel et al. (2007) evaluated the calibration of
hyperspectral gamma-ray energy spectra to predict various surface and subsurface
28
A. Manickavasagan and N. Yasasvy
Fig. 2.8 Transportation 107Cd in shoots of six rice cultivars (vegetative stage). a Photograph
of test plants. The large-dotted rectangle indicates the FOV of PETIS. b Serial images of Cd
movement (0–36 h). c Time course of Cd amounts in ROI-1 (shoot bases). d Time course of Cd
amounts in ROI-2 (leaf sheaths and leaf blades). The relevant portion of each ROI is surrounded
by red lines in the black and white photograph. Cd in ROI-1(pmol) and Cd in ROI-2(pmol) indicate the sums of radioactive107Cd and non-radioactive Cd (Ishikawa et al. 2011)
soil properties. On-the-go gamma-ray spectrometer (GR 320 portable g­ amma-ray
­spectrometer, ExploraniumTM Radiation Detection Systems Toronto, Canada) was
used in this study. The gamma-ray spectrometer was mounted in a wooden cradle
on the front of a four wheel drive vehicle for the on-the-go field measurements,
and the vehicle was driven at 3 m/s. The hyperspectral information c­onsisted
of 256 energy bands in the energy spectrum ranging from 0 to 3 MeV. It was
recorded every second at more than 20,000 sites in two fields in Australia. The
data were used to calibrate for the prediction of selected soil properties with the
2
Gamma-Ray Imaging
29
Fig. 2.9 Transportation 107Cd in panicle for Koshihikari and BIL48. BIL48 carries the QTL
responsible for high Cd accumulation derived from Jarjan with the Koshihikari genetic background. a Photograph of test plants. The large-dotted rectangle indicates the FOV of PETIS. b
Serial images of Cd accumulation in the panicle (0–36 h). c Time course of Cd amounts in ROI-3
(neck nodes of panicles). d Time course of Cd amounts in ROI-4 (panicles). The Cd in ROI-4
refers to the Cd amount per glumous number. The relevant portion of each ROI is surrounded by
red lines in the black and white photographs. Cd in ROI-3 (pmol) and Cd in ROI-4 (pmol) indicate the sums of radioactive 107Cd and non-radioactive Cd (Ishikawa et al. 2011)
bootstrap aggregation with partial least squares regression. It was reported that the
developed model provided robust prediction of clay, course, sand, and Fe contents
in the 0–15-cm soil layer and pH and course sand contents in the 15–50-cm soil
layer. It was also mentioned that proximally sensed gamma-ray spectrometry has
potential for predicting soil properties in different soil landscapes.
30
A. Manickavasagan and N. Yasasvy
Conclusions
Gamma-ray imaging techniques (both 2-D and 3-D) have been mainly utilized in
the quantification and visualization studies within plants such as water uptake and
transportation, metal uptake and transportation, photoassimilate translocation, and
so on. Although applications of this technique are mainly used for research and
development work, it has great potential to serve as a tool for the development of
various plant varieties and management practices for a wide range of agro-climatic
conditions to improve the productivity.
Acknowledgements We thank The Research Council (TRC) of Sultanate of Oman for funding
this study (Project No. RC/AGR/SWAE/11/01—Development of Computer Vision Technology
for Quality Assessment of Dates in Oman).
References
Beer S, Streun M, Hombach T, Buehler J, Jahnke S, Khodaverdi M, Larue H, Minwuyelet S, Parl
C, Roeb G, Schurr U, Ziemons K (2010) Design and initial performance of PlanTIS: a highresolution positron emission tomograoh for plants. Phys Med Biol 55:635–646
Converse AK, Ahlers EO, Bryan TW, Williams PH, Barnhart TE, Engle JW, Nickles RJ,
DeJesus OT (2012) Positron emission tomography (PET) of radiotracer uptake and distribution in living plants: methodological aspects. J Radioanal Nucl Chem. doi: 10.1007/
s10967-012-2383-9
Fujimaki S, Suzui N, Ishioka NS, Kawachi N, Ito S, Chino M, Nakamura S (2010) Tracing cadmium from culture to spikelet: noninvasive imaging and quantitative characterization of
absorption, transport, and accumulation of cadmium in an intact rice plant. Plant Physiol
152:1796–1806
Furukawa J, Yokota H, Tanoi K, Ueoka S, Matsuhashi S, Ishioka NS, Watanabe S, Uchida H,
Tsuji A, Ito T, Mizuniwa T, Osa A, Sekine T, Hashimoto S, Nakanishi TM (2001) Vanadium
uptake and an effect of vanadium treatment on 18F-labeled water movement in a cowpea plant by positron emitting tracer imaging system (PETIS). J Radioanal Nucl Chem
249:495–498
Garbout A, Munkholm LJ, Hansen SB, Petersen BM, Munk OL, Pajor R (2012) The use of PET/
CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions. Plant Soil 352:113–127
Hirose A, Yamawaki M, Kanno S, Igarashi S, Sugita R, Ohmae Y, Tanoi K, Nakanishi TM (2013)
Development of a 14C detectable real-time radioisotope imaging system for plants under
intermittent light environment. J Radioanal Nucl Chem 296:417–422
Ishikawa S, Suzui N, Ito-Tanabata S, Ishii S, Igura M, Abe T, Kuramata M, Kawachi N, Fujimaki
S (2011) Real-time imaging and analysis of differences in cadmium dynamics in rice cultivars (Oryza sativa) using positron-emitting107Cd tracer. BMC Plant Biol 11:1–12
Kawachi N, Sakamoto K, Ishii S, Fujimaki S, Suzui N, Ishioka NS, Matsuhashi S (2006) Kinetic
analysis of carbon-11-labeled carbon dioxide for studying photosynthesis in a leaf using positron emitting tracer imaging system. IEEE Trans Nucl Sci 53:2991–2997
Kawachi N, Kikuchi K, Suzui N, Ishii S, Fujimaki S, Ishioka NS, Watabe H (2007) Imaging of
carbon translocation to fruit using carbon-11-labeled carbon dioxide and positron emission
tomography. IEEE Trans Nucl Sci 58:395–399
Kawachi N, Sakamoto K, Ishii S, Fujimaki S, Suzui N, Ishioka NS, Matsuhashi S (2005)
A method to quantitate photosynthetic rate constant within leaf using carbon-11-labeled
2
Gamma-Ray Imaging
31
carbon dioxide and positron emitting tracer imaging system. IEEE nuclear science symposium conference record, pp J03–35
Kikuchi K, Ishii S, Fujimaki S, Suzui N, Matsuhashi S, Honda I, Shishido Y, Kawachi N (2008)
Real-time analysis of photoassimilate translocation in intact eggplant fruit using 11CO2 and a
positron-emitting tracer imaging system. J Jpn Soc Hortic Sci 77:199–205
Kiyomiya S, Nakanishi H, Uchida H, Nishiyama S, Tsukada H, Ishioka NS, Watanabe S, Osa A,
Mizuniwa C, Ito T, Matsuhashi S, Hashimoto S, Sekine T, Tsuji A, Mori S (2001) Light activates H15
2 O flow in rice: detailed monitoring using a positron-emitting tracer imaging system
(PETIS). Physiol Plant 113:359–367
Kume T, Matsuhashi S, Shimazu M, Ito H, Uchida H, Tsuji A, Shigeta N, Matsuoka H, Osa A,
Sekine T (1997) Uptake and transport of positron-emitting tracer in irradiated plants. Dev
Plant Soil Sci 78:169–170
Matsuhashi S, Fujimaki S, Kawachi N, Sakamoto K, Ishioka NS, Kume T (2005) Quantitative
modeling of photoassimilate flow in an intact plant using the positron emitting tracer imaging
system (PETIS). Soil Sci Plant Nutr 61:417–423
Mori S, Kiyomiya S, Nakanishi H, Ishioka NS, Watanabe S, Osa A, Matsuhashi S, Hashimoto
S, Sekine T, Uchida H, Nishiyama S, Tsukada H, Tsuji A (2000) Visualization of 15O-water
flow in tomato and rice in the light and dark using a positron-emitting tracer imaging system
(PETIS). Soil Sci Plant Nutr 46:975–979
Nakamura S, Suzui N, Nagasaka T, Komatsu F, Ishioka NS, Tanabata SI, Kawachi N, Rai H,
Hattori H, Chino M, Fujimaki S (2013) Application of glutathione to roots selectively inhibits cadmium transport from roots to shoots in oilseed rape. J Exp Bot 64:1073–1081
Nakanishi TM, Yokota H, Tanoi K, Ikeue N, Okuni Y, Furukavwa J, Ishioka NS, Watanabe S,
Osa A, Sekine T, Matasuhashi S, Ito T, Kume T, Uchida H, Tsuji A (2001) Comparison of
150-labeled and 18F-labeled water uptake in a soybean plant by PETIS (positron emitting
tracer imaging system). Radioisotopes 50:265–269
Richards A (2001) X rays and gamma rays: crookes tubes and nuclear light. In Alien Vision:
exploring the electromagnetic spectrum with imaging technology. SPIE Press, Washington
Rossel RAV, Taylor JJ, Mcbratney AB (2007) Multivariate calibration of hyperspectral g-ray
energy spectra for proximal soil sensing. Eur J Soil Sci 58:343–353
Saha GB (2006) Physics and radiobiology of nuclear medicine. Springer, New York
Streun M, Beer S, Hombach T, Jahnke S, Khodaverdi M, Larue H, Minwuyelet S, Parl C, Roeb
G, Schurr U, Ziemons K (2007) PlanTIS: a positron emission tomograph for imaging 11C
transport in plants. IEEE nuclear science symposium conference record, pp M22–1
Suwa R, Fujimaki S, Suzui N, Kawachi N, Ishii S, Sakamoto K, Nguyen NT, Saneoka H,
Mohapatra PK, Moghaieb RE, Matsuhashi S, Fujita K (2008) Use of positron-emitting tracer
imaging system for measuring the effect of salinity on temporal and spatial distribution of
11C tracer and coupling between source and sink organs. Plant Sci 175:210–216
Tsukamoto T, Nakanishi H, Kiyomiya S, Watanbe S, Matsuhashi S, Nishizawa NK, Mori S
(2006) 52Mn translocation in barley monitored using a positron-emitting tracer imaging system. Soil Sci Plant Nutr 52:717–725
Tsukamoto T, Nakanishi H, Uchida H, Watanabe S, Matsuhashi S, Mori S, Nishizawa NK (2009)
52Fe translocation in barley as monitored by a positron emitting tracer imaging system
(PETIS): evidence for the direct translocation of Fe from roots to young leaves via phloem.
Plant Cell Physiol 50:48–57
Tsukamoto T, Uchida H, Nakanishi H, Nishiyama S, Tsukada H, Matsuhashi S, Nishizawa
NK, Mori S (2004) H15
2 O translocation in rice was enhanced by 10 µm 5-aminolevulinic
acid as monitored by positron emitting tracer imaging system (PETIS). Soil Sci Plant Nutr
50:1085–1088
Watanabe S, Iida Y, Suzui N, Katabuchi T, Ishii S, Kawachi N, Hanaoka H, Watanabe S,
Matsuhashi S, Endo K, Ishioka NS (2009) Production of no-carrier-added 64Cu and applications to molecular imaging by PET and PETIS as a biomedical tracer. J Radioanal Nucl
Chem 280:199–205
Chapter 3
X-ray Imaging
C. Karunakaran and D. S. Jayas
Introduction
X-rays were discovered by a German physicist Wilhelm Conrad Röntgen in 1895
(Cullity 1978; Selman 2000). He was awarded the first Nobel Prize in physics for
the discovery of X-rays also referred to as Röntgen rays. After its discovery, the
potential use of X-rays for medical and materials science was quickly recognized
and investigated by different research groups. However, the use of X-rays for agricultural and food product inspection started only in the 1920s (Yuasa 1926; Fenton
and Waite 1932).
X-rays are a form of electromagnetic radiation and have more penetration
power and shorter wavelengths than visible light. They travel through vacuum
in the form of waves in straight lines and are invisible to naked eyes. The wavelengths of X-rays are much shorter than IR and UV rays and are in the range
from ~10–0.01 nm (~100 eV–100 keV) (Attwood 1999). Although there is no
exact boundary, X-rays with longer wavelengths (of approximately 10–0.6 nm)
are called soft X-rays and X-rays with shorter wavelengths (of approximately
0.6–0.01 nm) are called hard X-rays. Hard X-rays have higher energy and penetration power than the soft X-rays, and hence hard X-rays are used to inspect highdensity materials such as metals in food products. Soft X-rays on the other hand
have low energy and penetration power. They are used for X-ray inspection of
low-density materials such as agricultural fruits, vegetables, and seeds.
C. Karunakaran (*)
Canadian Light Source Inc., University of Saskatchewan,
44 Innovation Boulevard, Saskatoon, SK, S7N 2V3, Canada
e-mail: Chithra.Karunakaran@lightsource.ca
D. S. Jayas
Department of Biosystems Engineering, University of Manitoba,
Winnipeg, MB R3T 2N2, Canada
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_3, © Springer-Verlag Berlin Heidelberg 2014
33
C. Karunakaran and D. S. Jayas
34
Fig. 3.1 Schematic of a
typical X-ray tube
X-ray
transparent
port
+
X-ray beam
Electron stream
−
Tube current
&
Voltage
Filament
Cathode
Production of X-rays
X-rays are produced when fast moving electrons from a hot cathode impinge on a
heavy metal target. A typical X-ray tube consists of a filament-type cathode and a
metal target called an anode in an evacuated tube (Fig. 3.1). Gas-type X-ray tubes
with negative and positive charge electrodes are obsolete, and filament-type cathodes usually made of tungsten are now used (Cullity 1978; Selman 2000). The
tungsten filament must be heated to a minimum temperature of 2,200°C to emit
electrons (Curry III et al. 1990). The electrons produced by the cathode maintained at a high negative potential are attracted toward the anode maintained at
ground potential under vacuum. The accelerated electrons though a large potential
strike the target and produce X-rays.
The X-rays are emitted from a narrow region on the target called the focal spot
in all directions and escape from the tube. Most of the kinetic energy of the electrons striking the target is converted into heat while only less than 1 % is converted into X-rays (Cullity 1978). Therefore, the efficiency of the X-ray tubes is
only about 0.6 % (Selman 2000). The common target material used is tungsten due
to its high atomic number, thermal conductivity, and melting point. The higher the
atomic number of the target, the greater will be the efficiency in the production of
X-rays and the target material partly determines the quantity, quality, and energy
of X-rays produced (Curry III et al. 1990).
Until 1970, X-rays were produced only by laboratory-based X-ray tubes. In the
1970s, the production of X-rays by accelerating electrons in large storage rings called
synchrotrons were realized (Attwood 1999; Als-Nielsen and McMorrow 2011).
The X-rays from the synchrotrons have unique properties such as high intensity and
3
X-ray Imaging
35
Fig. 3.2 Bremsstrahlung and
characteristic radiations from
a X-ray tube. Reproduced
with permission from
Attwood (1999)
specific wavelengths compared to laboratory-based X-ray machines. The production,
properties, and applications of synchrotron-based X-rays are beyond the scope of this
book chapter.
Characteristics of X-rays
The deceleration and interaction of electrons with the anode results in the production of X-rays. X-rays produced by X-ray tubes are characterized by two types
of radiations: Bremsstrahlung and characteristic radiations as shown in Fig. 3.2.
Bremsstrahlung radiations are produced by the decelerating electrons due to the
anode. The ionization process created by the interaction of low-energy electrons
with the anode creates the characteristic radiation. The Bremsstrahlung radiation has a wide and continuous wavelength or energy range; however, the maximum intensity of Bremsstrahlung radiation is much lower than the characteristic
radiation. Characteristic radiation on the other hand has a narrow and discrete
wavelength range and has very high intensity. The wavelength of characteristic
radiation is dependent on the type of anode material used. Characteristic radiation
may not be produced if the applied tube voltage is less than the ionization energy
of the anode material.
The important characteristics of X-rays are their energy and intensity. The
potential applied (kV) between the cathode and anode determines the kinetic
energy or speed of the electrons and hence the energy or penetration power of
X-rays. The kinetic energy of the electrons produced by the cathode is given by:
E = eV
where
e electron charge (1.60 × 10−19 C);
V tube voltage (V).
(3.1)
C. Karunakaran and D. S. Jayas
36
The tube current determines the number of electrons flowing per second from
the filament toward the target. Therefore, the tube current determines the intensity
or amount of X-rays produced.
An X-ray beam consists of X-rays with a range of wavelength (poly-energetic)
that depends on the tube voltage and filter type used in the X-ray beam path. The
minimum wavelength of an X-ray photon is determined using the formula given
by (Curry III et al. 1990):
min =
1.24
V
(3.2)
Therefore, an X-ray photon beam will have a minimum wavelength of
1.24 × 10−2 nm if the tube potential is 100 kV. In short, hard X-rays and soft X-rays
can be produced by varying the applied tube voltage. Hard X-rays are produced by
high tube voltage and filters of materials with high atomic number (e.g., copper)
which absorb the low-energy soft X-rays. Soft X-rays are produced by low tube voltage and filters of materials with low atomic number such as aluminum and beryllium
(Selman 2000). The use of cobalt as the target material and beryllium window in the
X-ray tube produces soft X-rays, which can be used to X-ray low-density materials
such as agricultural seeds and grains. The shielding of low-energy X-rays is difficult
and hence is expensive than the shielding of high-energy X-ray machines.
Interaction of X-rays with Matter
X-rays, when come in contact with matter, are either absorbed or scattered or
transmitted. This loss of intensity of X-rays by absorption and scattering is called
‘attenuation.’ The intensity of X-rays as it passes through medium decays exponentially and the residual or transmitted intensity through the object are measured
by a detector. The intensity of transmitted X-rays depends on mass density and
absorption coefficient of the materials being X-rayed. The intensity of a transmitted X-ray beam through a medium is given by:
I = Io e−µav X
(3.3)
where
I intensity of the transmitted beam, Gy;
Io intensity of the incident beam, Gy;
μav the average linear absorption coefficient of the medium, m−1; and
X thickness of the medium, m.
The value μav is the mean value of linear absorption coefficient value for all
wavelengths in the X-ray beam. The linear absorption coefficient of a medium is
proportional to its density and is higher for elements with high atomic number but
is independent of its physical state. The ratio of µ/ρ is called as mass absorption
coefficient, where ρ is the density of the medium or sample in kg/m3.
3
X-ray Imaging
37
Detection
screen
Computer
display
Sample
X-ray machine
X-ray control unit
X-ray tube
Fig. 3.3 Photograph of a 2D X-ray machine and its internal view showing X-ray tube, sample,
and detector
The mass absorption coefficient is dependent on the energy or wavelength of
X-rays, and the coefficient represents the mass absorption coefficient of water plus
solids in the material. The average linear absorption coefficient of X-rays produced
at 50 kV potential and passed through 5 cm of water is 1 (George and Martin
1952). The X-ray wavelength should be selected based on the material type used in
the application. X-ray absorption coefficient of gases is negligible (Tollner 1993).
Inspection of smaller seeds like wheat for quality and insect infestations
require voltage of about 10–25 kV and current of 10 µA–3 mA range (Stermer
1972; Schatzki and Fine 1988; Keagy and Schatzki 1991, 1993; Ron and David
2002; Haff and Slaughter 2004; Karunakaran et al. 2005; Neethirajan et al. 2007).
For relatively bigger seeds such as pecan or almonds and pistachio nuts with hard
shells, the voltage is increased from 25 to 50 kV with current in the few mA range
to increase the penetration power (Keagy et al. 1996; Kim and Schatzki 2001;
Kotwaliwale et al. 2007, 2009). For inspection of fruits such as apple 35–50 kV
and ~15 mA current are used, whereas fruits with hard shell seeds such as peach,
apricot, and mango, 25–70 kV and 300–1,000 mA are used (Han et al. 1992;
Thomas et al. 1993, 1995; Schatzki et al. 1997; Zwiggelaar et al. 1997; Kim and
Schatzki 2000). Detection of bone fragments from deboned poultry uses a setting of 30–40 keV and 16 mA (Tao and Ibarra 2000; Tao et al. 2001). To detect
metallic and non-metallic contaminants from a loaf of bread, 40 kV potential and
1.0 mA current is used (Morita et al. 2003).
Two- and Three-Dimensional X-ray Imaging
Two- (2D) or three-dimensional (3D) images of objects can be generated using
X-ray imaging systems. In 2D systems, the X-ray source, object, and detector are
all stationary and X-ray images are recorded after exposing the object for certain
duration of time to X-rays (Fig. 3.3). The 2D X-ray images are useful to determine
C. Karunakaran and D. S. Jayas
38
the presence of defects or contaminants; however, localization and size or volume
quantification of defects or contaminants cannot be precisely determined by analyzing the 2D X-ray images of 3D objects being X-rayed. In 3D X-ray imaging
system called as X-ray computed tomography (CT) method, one of the 3 components of the X-ray system (source, object, and detector) can be rotated in incremental angles from 0 to 180° while keeping the other two stationary to record
stack of X-ray images at different orientation of the object. These images can be
reconstructed to generate the 3D X-ray images of objects. This helps to determine
the quality and to locate defects of products precisely. The method is successfully
used to determine maturity, firmness, and bruises of fruits and vegetables and to
detect the presence of foreign materials in food products (Barcelon et al. 1999a, b;
Neethirajan et al. 2004, 2006b, 2008; Mousavi et al. 2007; Lin et al. 2008; Frisullo
et al. 2009).
In X-ray CT, the absorption of material is described by CT number defined as:
CT number =
µ − µw
·k
µw
(3.5)
where
µ linear absorption coefficient of the medium, m−1,
µw linear absorption coefficient of water, m−1; and
k a constant. If k is assumed as 1,000, then the CT number is called as Hounsfield
unit (Barcelon et al. 1999a).
Detection of X-rays
The process by which the transmitted X-ray beam through an object produces
details of the object on a photographic film, fluorescent screen, or counters is
called radiography. X-rays affect photographic film in the same way as visible light and the degree of blackening of the photographic film is proportional
to the intensity of the incident X-ray beam. Fluorescent screens are made of certain materials that fluoresce in the visible region when X-rays strike the screens.
X-ray counters are electronic devices that convert the incident X-ray beam into an
electric current. The number of current pulses per unit time is proportional to the
intensity of the incident X-ray beam on the counter. The proportional, scintillation,
and semiconductor are different types of electronic X-ray counters. The scintillator
screens convert incident X-ray energy into visible light and the visible images can
be recorded by a coupled charge device (CCD) camera attached to the scintillator
screen.
Different types of detectors from X-ray film to fluorescent screens have been
used in different X-ray imaging applications for agricultural and food products.
X-ray films were used in the earlier studies for manual inspection of products (Fenton and Waite 1932; Milner et al. 1950, 1952; Kirkpatrick and Wilbur
3
X-ray Imaging
(a)
39
(b)
(c)
Fig. 3.4 X–ray images of a wheat kernel infested by a live larva of Sitophilus oryzae (a and b)
recorded in less than a minute apart showing the movement of the larva. The subtracted image (c)
shows the difference between images a and b. Reproduced with permission from Karunakaran
et al. (2003a)
1965; Mills and Wilbur 1967; Sharifi and Mills 1971a, b; Stermer 1972; Schatzki
and Fine 1988; Keagy and Schatzki 1991; Thomas et al. 1993, 1995; Keagy et al.
1996; Schatzki et al. 1997; Kim and Schatzki 2001; Haff and Slaughter 2004).
X-ray counters were used in a few studies for the automated detection of defects
(Lenker and Adrian 1971; Stermer 1972; Schatzki et al. 1997). The availability
of fluorescent and scintillator screens have enabled it be coupled with digital
image processing for automated inspection of products for quality and defects
(Karunakaran et al. 2002, 2003b, 2004; Haff and Slaughter 2004; Neethirajan
et al. 2006a). For example, fluorescent or scintillator screens with digital image
processing have enabled to determine whether the insect infestations inside
agricultural products are due to live or dead insects. This information is vital to
determine whether the products need fumigation to control infestation or just cleaning to remove dead insects (Fig. 3.4).
Applications of X-rays in Agricultural Products
Automated harvesting, handling, processing of agricultural and food products, and
increased consumer awareness have forced industries to inspect every product for
quality before marketing. Agricultural and food products are biological entities
and hence are liable to a wide variety of damages or spoilage during production,
handling, processing, and storage until they reach consumers. Inconsistent internal
composition, internal voids, insect or frost damages occur during production; cracks
and bruises occur during post-harvest handling; foreign materials such as stone,
metal, or plastic pieces may get mixed with food products during handling and processing; and missing or misshapen items can occur in packaged foods. Products
with defects and foreign materials that affect the quality of products have to be
40
C. Karunakaran and D. S. Jayas
removed to meet product standards. To determine the internal (composition, sugar
content, water content, acidity, firmness, texture, defects, and damages) and external
(color, shape, size, texture, defects, and damages) characteristics of agricultural and
food products, different destructive and non-destructive methods are presently used
(Schatzki and Wong 1989; Gunasekaran 2001; Brosnan and Sun 2004; Karunakaran
and Jayas 2005; Jayas et al. 2007).
The advantage of non-destructive methods is that every product can be inspected
for quality assurance without destroying the product. Some of the non-destructive
techniques for quality evaluation of products use magnetic resonance (MR), X-rays,
ultraviolet (UV), visible light, near-infrared (NIR) radiation, microwaves, sonic,
and ultrasonic methods (Kim et al. 2001; Gruwel et al. 2002; Lu and Ariana 2002;
Delwiche 2003; Andaur et al. 2004; Du and Sun 2004; Neethirajan et al. 2004;
Paliwal et al. 2004). Among these methods, X-ray imaging is considered one of
the excellent methods to determine the internal qualities of products and to inspect
packaged foods.
Internal Defects in Vegetables and Fruits
As soft X-ray absorption images are ideal to determine changes associated with
mass density variations, they are very suitable to determine structural discontinuities in objects such as voids, cracks, and internal damages. They have been successfully used to select products at the right maturity level, to identify products
with internal defects and to detect insect infestations (Diener et al. 1970; Lenker
and Adrian 1971; Han et al. 1992; Keagy et al. 1995).
Soft X-rays are used to determine maturity of tomato, peach, and lettuce by
measuring the density changes at different maturity levels (Lenker and Adrian 1971,
1980; Adrian et al. 1973; Bercht et al. 1991; Barcelon et al. 1999b). Lettuce head
becomes denser as it matures and X-ray method is successfully used to determine
the maturity. A photodiode operated in resistive mode was used to convert the transmitted X-rays through the lettuce heads into voltages that determined the maturity level. A mechanical harvester with an X-ray system to select matured lettuce
heads has consistent and better selection efficiency than human experts (Lenker and
Adrian 1971). The sensor used could detect a 5 % change in the lettuce head thickness that is equivalent to a 10 % change in the transmitted X-ray intensity and the
mechanical harvester harvested only 4 % soft heads compared to 13 % soft heads
picked by human experts. The X-ray CT imaging method was used to determine
composition such as soluble solids, acidity, pH, and moisture contents at different
ripening stages of peach (Fig. 3.5). It was determined that the X-ray CT numbers
have good correlation (with R2 value higher than 0.8) with the internal composition
of fruit values determined by analytical techniques (Barcelon et al. 1999a, b).
Hollow heart (discolored central cavity) which is most prevalent in large tubers in
potatoes is one of the factors determining the grade. Specific gravity and size separation methods were used, and both methods were ineffective in detecting the hollow
3
X-ray Imaging
(a)
41
(b1)
(b2)
(c1)
(c2)
Fig. 3.5 X-ray images of vegetables and fruits showing internal defects. a hollow heart in
potato; b1 and b2 normal and split-pit in peaches; and c1 and c2 peaches at different storage
times. Reproduced with permission from Finney and Norris (1978), Han et al. (1992), Barcelon
et al. (1999b)
hearts. The second derivative of the X-ray density curves along the longitudinal axis
for normal potatoes was in the range of +0.2 to −0.2 whereas hollow heart potatoes had values greater than 0.4, and this method identified hollow hearts in potatoes
with 100 % accuracy (Fig. 3.5) (Finney and Norris 1978). The density curve was a
measure of the transmitted X-ray through the potatoes represented by log(1/T) where
T is the ratio of X-ray intensity at the sensor without potatoes divided by the intensity recorded with potatoes. The second derivative values correlated well (correlation coefficient of 0.91) with the volume and size of the internal cavity. Split pits in
peaches reduce the shelf life, create problem during cutting for the canning process,
and have low consumer preference. Peaches with split pits are detected with 98 %
accuracy using simple thresholding process of the X-ray images (Han et al. 1992).
Variety differences do not interfere with identification accuracy, but peaches oriented
top to bottom or suture to back only reveal the presence of split pits.
The internal physiological disorders or biochemical changes of fruits that occur
during storage need to be detected non-destructively. Most of the changes are not
evident externally unless the damage is very severe. The internal disorder called
transparency due to increased sucrose level is not preferred by customers as well
as for the shelf life of fruits (Haff et al. 2006). The storage of fruits under refrigerated conditions may create chilling injury (dehydration) or core breakdown (tissue
discoloration) (Lammertyn et al. 2001, 2003). Detection of normal and extreme
translucent pineapples and chilling injury in stored nectarines have been demonstrated using X-ray imaging method (Haff et al. 2006; Sonego et al. 1995). The
core breakdown in pears stored for 6 months when determined using MR imaging and X-ray CT images revealed that MR images have better contrast between
unaffected and healthy tissues. Therefore, MR imaging method is more sensitive
to detect the core breakdown earlier during the storage conditions than the X-ray
CT system (Lammertyn et al. 2003).
The adaptability of X-ray technique to detect bruises, water core, and stem rot
in apples is reported in different studies (Diener et al. 1970; Tollner et al. 1992;
Schatzki et al. 1997; Kim and Schatzki 2000). A remote control mechanism was
used to align the fruits, and X-ray images were recorded using a line scan X-ray
detection system and high-quality radiographs (Diener et al. 1970). Bruises and
cracks that were not visible from outside the fruits are clearly seen in the line scan
42
C. Karunakaran and D. S. Jayas
detection system and radiographs. However, stem and calyx are often confused
with bruises. Bruises in the periphery of the apples are seen only when radiographs were superimposed on radiographs of fruits without bruises. The system
handled 30 apples per second. Recognition of bruises, water core, stem rot, and
moth damages in different cultivars from the radiographs by the trained persons
was determined (Schatzki et al. 1997). Identification accuracy was more than 50 %
when the experts were presented with still images of scanned X-ray radiographs
but declined to less than 25 % when the images were scrolled down the screen
at increasing rates. Moth damage in apples was identified 8–19 d after the entry
of larvae into the fruits. Identification of watercore in apples was successful with
only 5–8 % false positives only when the apples were oriented in a fixed and same
angle with respect to the incoming X-ray beam direction (Kim and Schatzki 2000).
Internal Defects in Nuts
Aflatoxin, a potential carcinogen is determined to be present in the nuts (almonds,
pistachios, etc.) with split hulls before harvest and in insect-infested nuts (Keagy
et al. 1995; Kim and Schatzki 2001). Standard grades restrict the presence of insect
damages to 1–3 % by weight in pistachio nuts during grading. Suspected pistachio nuts removed by visual inspection by humans at the end of processing stream
are reported to contain 89 ng of aflatoxin/g of nuts where the permissible level is
only 4 ng/g for US No:1 grade (Keagy et al. 1995; Casasent et al. 1996). Infested
almonds were identified with 81 % accuracy when images from X-ray films
scanned at a resolution of 0.17 mm2/pixel were analyzed using image processing
algorithms (Fig. 3.6). The identification accuracy dropped to 65 % when real-time
images recorded with sensor resolution of 0.5 mm2/pixel were analyzed (Kim
and Schatzki 2001). The false positives also increased to 9 % as compared to 1 %
with radiographic images. The algorithm is fast enough to inspect 66 nuts/s but to
implement this technique in industry; a high-resolution X-ray sensor is required.
Different identification percentages (83–90 %) were obtained when the histogram
features and moments of the raw and edge-enhanced images of the pistachio nuts
were used as features (Keagy et al. 1995). Variability in a human inspection system
was demonstrated by the recognition levels of six subjects ranging from 83.1 to
91.7 % in analyzing the X-ray images of pistachio nuts (Keagy et al. 1996), but
the classification accuracy was better than the machine recognition. The low recognition by the machine might be due to low resolution of the X-ray images used
during analysis (radiographs scanned at a resolution of 0.173 mm2/pixel were converted to images with 0.5 mm2/pixel resolution to match the available sensors).
Pecans and chestnuts have hard shells and hence are difficult to inspect the
quality of nuts visually. The use of X-ray imaging system to determine the nut
weight and insect damages in pecan nuts is demonstrated by incorporating nut
meal of different weights and insect damages into pecan nut shells that were cut
open and glued together before imaging (Fig. 3.6) (Kotwaliwale et al. 2007, 2009).
It was determined that the nutmeat weights determined from the X-ray images was
3
X-ray Imaging
(a1)
43
(a2)
(b1)
(b2)
Fig. 3.6 X-ray images of un-infested and severely insect-infested almonds (a1 and a2) and
peacans (b1 and b2). Reproduced with permission from Kim and Schatzki (2001), Kotwaliwale
et al. (2007)
within an error of 10 % and insect damages were only visible after improving the
contrast of the X-ray images due to the uneven meat nature of pecan nuts.
Insect Infestation in Fruits
Insect infestations in fruits are complex as infestations may not be visible outside
and cannot be easily identified manually. The insect eggs develop from the flowering stage and mostly develop inside the fruit’s seed. The infestation may later on
lead to progressive damage to the fruits. Quarantine of agricultural products requires
inspection of each and every fruit. The feasibility to identify insects in mango, apple,
peach, guava, and olives has been reported by different groups (Thomas et al. 1995;
Lin et al. 2005; Jackson and Haff 2006; Jiang et al. 2008). The infestation of fruits
by oriental fruit fly was identified manually after 2–3 days of egg implantation
inside the fruits whereas the infestation was very obvious from the X-ray images
only after 6 days of infestation (Fig. 3.7) (Lin et al. 2005; Jiang et al. 2008).
Mango seed weevil is a serious pest, and identification will improve the reliability of export market and processing industries. Correct identification of all weevil
damages in mangoes has been reported (Thomas et al. 1995). It was determined
that sometimes surface damages in olives may be mistaken for insect infestations,
but X-ray images revealed the internal structures clearly (Fig. 3.7) (Jackson and
Haff 2006). It was determined that the visual inspection of X-ray images can detect
even small damages. The automatic machine recognition algorithm needs to be
improved to increase the detection percentage and to reduce the false positives.
Internal Defects in Seeds
Extensive use of soft X-rays in seeds to study the anatomy and to determine the
viability, dormancy, and internal damages is reported in the literature (Fig. 3.8)
(Stermer 1960; Belcher 1968b; Ciecero et al. 1998). Extension and localization of
mechanical damage in maize seeds, which cannot be determined by other testing
C. Karunakaran and D. S. Jayas
44
(a)
(b1)
(b2)
Fig. 3.7 X-ray images of insect-infested peach (a); and un-infested and infested olives (b1 and b2).
Reproduced with permission from Lin et al. (2005), Jackson and Haff (2006)
(a1)
(a2)
(b1)
(b2)
Fig. 3.8 X-ray images of wheat a1 healthy; a2 sprouted; b1 vitreous; b2 nonvitreous. Reproduced
with permission from Neethirajan et al. (2007), Neethirajan et al. (2006c)
methods, was clearly seen in the X-ray radiographs (Ciecero et al. 1998). It was
determined that the mechanical damage not affecting the embryonic axis does not
affect the germination of maize seeds. Differentiation of sprouted kernels from
healthy wheat kernels and vitreous from nonvitreous wheat kernels were determined using X-ray imaging method (Neethirajan et al. 2006a, c, 2007).
Insect Infestations in Grain
The use of X-ray technique to detect infestations in grain gained momentum in
1926 as it is considered an efficient method in detecting infestation due to borers
in grain (Yuasa 1926). X-rays were then used to detect internal insect infestations in cotton seeds (Fenton and Waite 1932). Presence of pink bollworms in cotton seeds can be detected by examining X-ray images of a single layer of cotton
seeds with a hand lens. Imperfect and infested seeds are not distinguished easily,
but experience in examining the films increases a person’s ability to see the difference. Soft X-rays have been used in several studies to detect internal insect
infestations in seeds and cereal grains by manually analyzing X-ray radiographs
(Yuasa 1926; Fenton and Waite 1932; Milner et al. 1950, 1952; Stermer 1972;
Schatzki and Fine 1988; Haff and Slaughter 2004). Use of X-rays to detect infestations due to Sitophilus oryzae (L.), Sitophilus granarius, and Sitotroga cerealella
3
X-ray Imaging
Larva
45
Larva
Pupa
Adult
Insect damaged kernel
Fig. 3.9 X–ray images of wheat kernels infested by different life stages of Sitophilus oryzae.
Reproduced with permission from Karunakaran et al. (2003a)
in wheat; S. oryzae and S. cerealella in corn; S. oryzae in rough and milled rice;
Acanthoscelides obtectus (Say) and Callosobruchus maculatus (F.) in cowpeas;
and A. obtectus in pinto beans and kidney beans; Sitophilus zeamais, S. oryzae,
S. cerella, and Rhyzopertha dominica (F.) in wheat and corn is demonstrated by
different researchers (Milner et al. 1950, 1952; Schatzki and Fine 1988; Keagy
and Schatzki 1991; Karunakaran et al. 2002, 2003a). Infestations caused by
Cryptolestes ferrugineus (Stephens) and Tribolium castaneum larvae were correctly identified with more than 81 % accuracy; more than 97 % of kernels infested
by Plodia interpunctella, S. oryzae, and R. dominica larvae were correctly identified; and all kernels infested by S. oryzae, and R. dominica pupae-adults were correctly identified (Fig. 3.9) (Karunakaran et al. 2004).
Among different methods used to detect insect infestations in grain, soft X-ray
method is recognized as the simple and fast method to detect hidden insects in
grain (Keagy and Schatzki 1991; Schatzki et al. 1993). X-ray method is extensively (40 % in the US mills) used to determine insect infestations and has been
determined that X-ray images can reveal even the insect plugs in grain kernels
(Schatzki and Fine 1988). Keagy and Schatzki (1991) determined the effect of
image resolution to detect infested grain kernels. The X-ray radiographs were
scanned and digitized at different resolutions of 32.8, 65.6, 131.2, and 262.4 μm
per pixel, and the recognition levels by trained persons were recorded. Best recognition level was achieved when the image resolution was 65.6 µm of film per pixel.
Therefore, for real-time recording of X-ray images, the sensors in the X-ray detection system should not be larger than 65.6 µm in size.
Developmental Behavior of Insects
Application of X-rays to detect insect infestation made dramatic improvement
in studying the developmental behavior of internally feeding insects in grain.
Until then, development and behavior of internally developing insects in grain
was studied by dissecting the infested kernels and hence were not continuous
and remained a mystery. The developmental behavior of Sitophilus sasakii Tak.
46
C. Karunakaran and D. S. Jayas
in wheat (Pedersen and Brown 1960); S. cerealella in wheat, sorghum, and corn
(Mills and Wilbur 1967); S. zeamais Motschulsky and S. oryzae in wheat (Sharifi
and Mills 1971a, b) provides useful information about using the X-ray technique
to detect infested grain. Grain kernels infested by introducing adult insects or
newly hatched larvae in the grain were X-rayed daily till the adults emerged from
the grain kernels. The radiographs mounted in order were used to determine the
oviposition or larval entry position, feeding habits, size of different insect stages,
and length of developmental stages of insects (Mills and Wilbur 1967; Sharifi
and Mills 1971a, b). Oviposition sites and first instar larvae are most difficult to
identify at times, but tracing back the same kernel helped to identify them. The
embryo and hairs in the grain kernel and the body segments of the fourth instar
larva of S. sasakii were clearly seen from the wheat kernel images taken with an
X-ray microscope (exposure time—45 s; voltage—20 kV; and current—40 mA)
(Pedersen and Brown 1960). But, the radiographs taken with an X-ray machine
of 20 kV potential, 2 mA current, and 1.5 min exposure time did not show much
details.
X-rays in Food Products
Different studies have determined the inherent potential of X-ray technology to reveal defects and contaminants in food products (Ogawa et al. 1998;
Anonymous 1999; Johnson 2001a, b; Jing et al. 2003a, b). The advantage of the
technology is that it can detect metallic and nonmetallic contaminants such as
metals, bone, glass, stone, plastics, and rubber in food products (Schatzki et al.
1996; Zwiggelaar et al. 1996, 1997). The metallic and nonmetallic contaminants
have different densities than food materials and processed foods have more or
less uniform thickness (unlike agricultural products); these characteristics make
X-ray inspection system very attractive in the food industries. Figure 3.10 shows
an X-ray image of a deboned chicken fillet revealing the presence of small bone
fragments that are hidden inside. A thickness-compensated algorithm to detect
bone fragments from deboned poultry carcass that has uneven thickness was
developed (Tao and Ibarra 2000). The contaminants in food products are commonly detected by considering the rate of change of image gray levels if the
product under inspection has varying thickness (Ketch 1998; Anonymous 1999).
Using the same principle, presence of steel wires that were less than 0.8-mmdiameter and 3-mm-diameter stone, bone, and glass pieces was identified with
100 % accuracy from food products (Ketch 1998). In addition to detecting contaminants, X-rays are extensively used to detect carcass composition such as total
meat, fat, bone weights, and meat tenderness (Brienne et al. 2001; Marcoux et al.
2005; Karamichou et al. 2006; Kröger et al. 2006; Navajas et al. 2010; Prieto et al.
2010). Metallic and nonmetallic contaminants from a loaf of bread and hamburger
steak were identified after applying image processing to the X-ray images (Morita
et al. 2003).
3
X-ray Imaging
(a)
47
(b)
Fig. 3.10 X-ray images of a a deboned chicken fillet showing the presence of small bone fragments, and b a hamburger steak showing metallic and nonmetallic contaminants. Reproduced
with permission from Tao et al. (2001), Morita et al. (2003)
Biological Effects of X-rays
Human Beings
The X-ray intensity is defined as the radiation intensity required to generate
­ionization charge of 2.58 × 10−4 C/kg (Röntgen or Roentgen) of air. The radiation amount absorbed per unit mass of material is called the absorbed dose and is
measured in grays (1 Gy = 100 rad = 1 J/kg). To determine the biological effect
of radiation, the absorbed dose is multiplied by a quality factor and expressed in
Sievert (1 Sv = 100 rem, rem is an older measurement unit). X-rays and gamma
rays have a quality factor of 1 (Robertson 1976).
Active cells and skin are more susceptible to damage due to radiation, and
mature adults have more resistance to radiation than children. The damage due
to a single large radiation dose is more than the same dose spread over a number of smaller doses. Exposure to direct X-ray beam has higher radiation intensity
risk than the scattered rays. The maximum permissible radiation levels for people
working with the radiation machines have been established by the International
Commission on Radiological Protection, ICRP (Robertson 1976). The regulatory radiation dosage established by ICRP for workers with radiation machines is
50 mGy/year.
Radiation from an X-ray machine operating at 120 kV provided with a 1.2-mm
lead barrier could not be distinguished from natural radiation (Tollner 1993). The
radiation dose, at 5 cm from the surface of a closed-cabinet X-ray system at its
maximum operating conditions of 50 kV potential and 200 µA current is less than
5 × 10−3 mGy/h (Lixi Inc., Downers Grove, IL). The exposure dose decreases as
the distance from the X-ray machine increases. This implies the expected personnel exposure for workers in the agricultural food inspection systems will be much
lower than the expected maximum dose.
Dosimeters measure cumulative radiation exposure of personnel working with
radiation instruments. The dosimeter measurements used in a study to detect insect
48
C. Karunakaran and D. S. Jayas
infestations in grain kernels by Karunakaran et al. (2003a, b, 2004) did not show
any exposed radiation dose. In the real-time operation of an automatic X-ray grain
inspection system, a worker might be exposed to a maximum radiation dose of
7.2 mGy/year (assuming a maximum exposure dose of 5 × 10 − 3 mGy/h; 6 h/day,
5 day/week, 4 week/month, 12 month/year) and this level is less than one-fifth of
the ICRP regulation.
Some interesting facts (Robertson 1976):
Natural radiation: background—174 mrad/year; cosmic rays—30 mrad/year;
uranium—50 mrad/year.
Television watching—1 mrad/h (black and white) and 2 mrad/h (color)
Medical X-ray—150 mrad/h (chest X-ray) and 20 mrad/h (dental X-ray)
Air travel—1 mrad/h.
Food Products
Irradiation of foods is regarded as a means to reduce food spoilage and increases
the food supplies to the developing countries and supply high-quality foods to
many developed importing countries. For example, irradiation prevents sprouting
in potatoes, onions, and garlic and hinders pest development in cereal grains and
spices. World Health Organization declares foods irradiated with doses less than
10 kGy is harmless for human consumption. However, less than 1,000 Gy is used
for irradiation of most food materials and higher doses are required for the disinfection of bacteria. In Canada, the maximum radiation dose used in cereal grain
and flour should not exceed 750 Gy (Jayas et al. 1995).
Significant damages to the nutrients of cereal grains occur if cereal grains are
exposed to 3,000–5,000 Gy of radiation. Even at low level 500 Gy, some starch
damages occur (Jayas et al. 1995). The X-ray intensities at different operating conditions are as follows: (1) potential—200 kV, distance—50 cm, current—30 mA,
wavelength—0.062 A, intensity—1.65 Gy/min (Haskins and Moore 1935);
(2) potential—15 kV, current—3 mA, intensity—0.04 Gy/min (Schatzki and Fine
1988). The operating voltage and current used for agricultural products inspection
are much lower, and hence, it implies that the products are exposed to less radiation. Agricultural products receive much greater radiation from the environments
during growth than during X-ray inspection (Tollner 1993).
Insects
Chemical treatment is the most successful method of controlling insects in grain.
Resistance development by insects and chemical residues in treated grain raises
increasing concern in the food products destined for human consumption. Hence, as
an alternative, radiation methods have been used to kill and sterilize insects to pre-
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49
vent and control insect infestation in grain and they leave no residues if reasonably
low dosages are used in the treatment.
Soft X-rays have no detrimental effect on the developmental behavior of insects
exposed to soft X-rays (Milner et al. 1950; Schatzki and Fine 1988). The adults of
S. granarius exposed to X-ray and magnetic fields (in the NMR spectroscopy study)
produced fertile eggs and developed into normal adults (Chambers et al. 1984).
Exposure of skins to radiation has less effect as they develop new cells often (Robertson
1976). Insects shed the outer body layer (frass) during their development from egg to
adult stage. Hence, it can be concluded that soft X-rays at low intensity level have no
deleterious effect on the exposed insects.
Plant Materials
Seeds treated with X-rays induce mutations and produce lethal effects (Belcher
1968a; Haskins and Moore 1935). Premature flowering, leaf discoloration, twisting, and duplication were observed in both wet and dry citrus seeds treated with
3–13 Gy of radiation (Haskins and Moore 1935). The seedlings of the seeds
exposed to 13 Gy perished shortly. The abnormalities in the seedlings might have
been caused by abnormal mitosis brought by the exposure to X-rays. Wort determined wheat kernels treated with 0.57–1.14 Gy exhibited accelerated growth rate;
heading and flowering; increased fresh and dry weights irrespective of seeds age
(9 and 57 mo) (Wort 1941). Alfalfa seeds treated at 107–638 Gy had delayed germination. However, no distinguished difference was exhibited 3 days after the
seedling emergence of the treated seeds. Seeds that received 213 Gy and more
produced damaged leaves and produced nonviable pollens suggesting exposure to
X-rays have detrimental effect (Davis and Hammons 1956).
Sax extensively reviewed the literatures on the effects of X-rays on plant
growth (Sax 1955). Simulating effects on the growth of seedlings, 30–100 %
increase in the yield of seeds exposed to lower doses of X-rays, deleterious effects
of seeds exposed to higher doses, and different sensitivity by different seeds to
ionizing radiation are reported in different studies. The author argues that these
results were obtained without inadequate controls and controlled tests repeated
with crops tested earlier in different studies failed to produce the reported results.
However, in his study, early flowering was determined in two varieties of gladiolus
seeds treated with irradiation dose of 40 Gy (Sax 1955).
Industrial Application of X-ray Technology
As discussed in the previous sections, several research works have shown that
x-ray technology has superlative use in detecting internal defects and contaminants in agricultural and food products. However, its use is exploited only in a few
C. Karunakaran and D. S. Jayas
50
contaminant
Fill height
missing item
(a)
misshapen item
(b)
(c)
Fig. 3.11 X-ray inspection of packed food products a canned fish, b packed hamburger patties,
c packaged noodle cup. Source Anonymous (2004)
industries for real-time quality inspection of products. Food industry is the second
largest user of X-ray technology for product quality inspection next to the electronics industry (Zuech 2001).
The increased consumer demand for processed foods and increased use of automation in food preparation has forced the industries to inspect processed food.
Most food products are inspected not only for contaminants but also completeness of contents in the packages. Until recently, operators or expert personnel were
involved in decision-making process from film-based or real-time X-ray images
of objects. This tedious process introduces substantial subjectivity in the decision process. Currently, the state-of-the-art system is machine-vision-based X-ray
systems where the software is used to enhance the quality of images and process
the images for the identification of contaminants or defects. Typical and compact
inspection systems are available for bulk solids, liquids or slurry, and packaged
products. The available industrial X-ray systems detect contaminants such as
glass, metal, stones, and plastics in fresh and frozen foods, chocolates, and snack
foods; product contaminants such as nut kernels and fruits stones in dry fruits; and
bones or foreign objects in meat and meat products.
X-rays were first used in 1965 in the food industry to inspect packaged chocolates by a Swedish manufacture to detect defects (Dearden 1996). Presently, the
technology is used to detect metallic and nonmetallic contaminants, completeness or missing and misshapen items, and excessive setting in packaged foods
(Fig. 3.11). It has been determined that X-ray is the only method available to
inspect foods packed in foil wrappers, aluminum trays, and glass or metallic containers. Industrial inspection systems are available that can inspect packaged foods
at the rate of 400 packs/min and jars or cans at the rate of 800 units/min.
The common use of X-ray inspection in the meat industry is to detect the presence of bones in fresh and processed products. In fish processing plants, X-ray
inspection is integrated in the processing lines to detect the presence of bones in
fish fillets coming out of automatic bone removers. Those fillets with the bones are
diverted from the main stream where manual workers are provided with computer
monitors that display the X-ray images with the bones highlighted for minimal
time of operation. One of the long-standing issues in X-ray inspection in the meat
3
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51
industry is the uneven thickness of the product under inspection. When a single
X-ray image of the product is obtained, a bone is confused with the thicker piece
of meat. This resulted in an inaccurate detection of bones and resulted in high false
positives. This problem is presently resolved by the dual-energy X-ray imaging
systems. In dual-energy X-ray imaging, the object is scanned twice at high and
low X-ray energies. Both images are subtracted to reveal the hidden bone fragments in objects of uneven thickness. X-ray inspection system for inspecting poultry pieces is capable of processing 20,000 pieces/h (Wilson 2002).
One of the recent advancement and utilization of X-ray technology is to determine the fat content of raw and processed meat products. Dual-energy X-ray
image of the products is used to determine chemical lean content of meat products
with a 1 % accuracy. X-rays are even used to detect bone fragments from ground
meat pumped through a pipe (Anonymous 1991).
Acknowledgment The Canadian Light Source is supported by the Natural Sciences and
Engineering Research Council of Canada, the National Research Council Canada, the Canadian
Institutes of Health Research, the Province of Saskatchewan, Western Economic Diversification
Canada, and the University of Saskatchewan. We thank the Natural Sciences and Engineering
Council of Canada and Canada Research Chairs Program for partial funding.
References
Adrian PA, Zahara M, Lenker DH, Goddard WB, French GW (1973) A comparative study of
selectors for maturity of crisphead lettuce. Trans ASAE 16:253–257
Als-Nielsen J, Mcmorrow D (2011) Elements of modern X-ray physics. Wiley, West Sussex, UK
Andaur JA, Guesalaga AR, Agosin EE, Guarini MW, Irarrazaval P (2004) Magnetic resonance
imaging for nondestructive analysis of wine grapes. J Agric Food Chem 52:165–170
Anonymous (1991) X-ray meat inspection scans up to nine tons per hour. Prepared Foods 160:87
Anonymous (1999) Keeping an eye out for those foreign bodies. Confectionery Prod 65:18–20
Anonymous (2004). http://sales.hamamatsu.com/en/home.php. Accessed 15 July 2004
Attwood D (1999) Soft X-rays and extreme ultraviolet radiation, principles and applications.
Cambridge University Press, Cambridge
Barcelon EG, Tojo S, Watanabe K (1999a) X-ray computed tomography for internal quality
evaluation of peaches. J Agric Eng Res 73:323–330
Barcelon EG, Tojo S, Watanabe K (1999b) X-ray CT imaging and quality detection of peach at
different physiological maturity. Trans ASAE 42:435–441
Belcher EW (1968a) Use of soft X-rays in tree seed testing and research. In: Proceedings of
Southeastern forest radiography workshop, University of Georgia, Athens, GA, pp 74–96
Belcher EW (1968b) Use of soft X-rays in tree seed testing and research. In: Proceedings of
Southeastern forest radiography workshop, University of Georgia, Athens, GA, pp 74–96
Bercht JK, Shewfelt RL, Garner JC, Tollner EW (1991) Using X-ray computed tomography to
nondestructively determine maturity of green tomatoes. HortScience 26:45–47
Brienne JP, Denoyelle C, Baussart H, Daudin JD (2001) Assessment of meat fat content using
dual energy X-ray absorption. Meat Sci 57:235–244
Brosnan T, Sun D (2004) Improving quality inspection of food products by computer vision—a
review. J Food Eng 61:3–16
Casasent D, Sipe MA, Schatzki TF, Keagy PM, Le LC (1996) Neural net classification of pistachio nut data. In: Proceedings of SPIE—the international society for optical engineering.
The Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, pp 217–227
52
C. Karunakaran and D. S. Jayas
Chambers J, Mckevitt NJ, Stubbs MR (1984) Nuclear magnetic resonance spectroscopy for
studying the development and detection of the grain weevil, Sitophilus granarius (L.)
(Coleoptera: Curculionidae), within wheat kernels. Bull Entomol Res 74:707–724
Ciecero SM, Heijden GWAMVD, Burg WJVD, BINO RJ (1998) Evaluation of mechanical
damage in seeds of maize (Zea mays L.) by X-ray and digital imaging. Seed Sci Technol
26:603–612
Cullity BD (1978) Properties of X-rays. In: Cullity BD (ed) Elements of X-ray diffraction.
Addison-Wesley, New York
Curry TS III, Dowdey JE, Murry RC Jr (1990) Christensen’s physics of diagnostic radiology. Lea
and Febiger Malvern, Pennsylvania
Davis RL, Hammons RO (1956) Reaction of alfalfa seedlings from dormant seeds subjected to
various dosages of X-rays. Agron J 48:529–530
Dearden R (1996) Automatic X-ray inspection for the food industry. Food Sci Technol Today
10:87–90
Delwiche SR (2003) Classification of scab- and other mold damaged wheat kernels by
near-infrared spectroscopy. Trans ASAE 46:731–738
Diener RG, Mitchell JP, Rhoten ML (1970) Using an X-ray image scan to sort bruised apples.
Agric Eng 51(356–357):361
Du C, Sun D (2004) Recent developments in the applications of image processing techniques for
food quality evaluation. Trends Food Sci Technol 15:230–249
Fenton FA, Waite WW (1932) Detecting pink bollworms in cottonseeds by the X-ray. J Agric Res
45:347–348
Finney EE, Norris KH (1978) X-ray scans for detecting hollow heart in potatoes. Am Potato J
55:95–105
Frisullo P, Laverse J, Marino R, Nobile MAD (2009) X-ray computed tomography to study
processed meat microstructure. J Food Eng 94:283–289
George RE, Martin WL (1952) Considerations in designing X-ray devices to grade frost damaged oranges. Department of Engineering, University of California, Los Angeles, CA
Gruwel MLH, Yin XS, Edney MJ, Schroeder SW, Macgregor AW, Abrams S (2002) Barley viability during storage: use of magnetic resonance as a potential tool to study viability loss.
J Agric Food Chem 50:667–676
Gunasekaran S (2001) Nondestructive food evaluation—techniques to analyze properties and
quality. Marcel Dekker Inc, New York
Haff RP, Slaughter DC (2004) Real-time X-ray inspection of wheat for infestation by the granary
weevil, Sitophilus granarius (L.). Trans ASAE 47:531–537
Haff RP, Slaughter DC, sarig Y, Kader A (2006) X-ray assessment of translucency in pineapple.
J Food Process Preserv 30:527–533
Han YJ, Bowers SV III, Dodd RB (1992) Nondestructive detection of split-pit peaches. Trans
ASAE 35:2063–2067
Haskins CP, Moore CN (1935) Growth modifications in citrus seedlings grown from X-rayed
seed. Plant Physiol 10:179–185
Jackson ES, Haff RP (2006) X-ray detection and sorting of olives damaged by fruit fly. In:
ASABE annual international meeting. American Society of Agricultural and Biological
Engineers, Portland, Oregon
Jayas DS, White NDG, Muir WE (1995) Stored grain ecosystems. Marcel Dekker Inc, New York
Jayas DS, Ghosh PK, Paliwal J, Karunakaran C (2007) Quality evaluation of wheat. In: Sun DW
(ed) Computer vision technology for food quality evaluation. Academic Press, New York
Jiang JA, Chang HY, Wu KW, Ouyang CS, Yang MM, Yang EC, Chen TW, Lin TT (2008) An
adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits.
Comput Electron Agric 60:190–200
Jing H, Chen X, Tao Y (2003a) Analysis of factors influencing the mapping accuracy of X-ray
and laser range images in a bone fragment detection system. In: ASABE annual meeting,
Las Vegas, Neveda
3
X-ray Imaging
53
Jing H, Chen X, Yang T (2003b) Geometrical calibration and integration of laser 3D and X-ray
dual systems. In: ASAE annual international meeting. American Society of Agricultural and
Biological Engineers, Las Vegas, Nevada
Johnson A (2001a) How do you value product integrity? Confectionery Prod 67:14–15
Johnson A (2001b) Visionary food safety. Food Process 70:14
Karamichou E, Richardson RI, Nute GR, Mclean KA, Bishop SC (2006) Genetic analyses of
carcass composition, as assessed by X-ray computer tomography, and meat quality traits in
Scottish Blackface sheep. Animal Sci Int J Fundam Appl Res 82:151–162
Karunakaran C, Jayas DS (2005) Machine vision system in postharvest technology. Stewart
Postharvest Rev 2:2
Karunakaran C, Jayas DS, White NDG (2002) Soft X-ray inspection of wheat kernels infested
by Sitophilus oryzae. In: ASAE annual international meeting/CIGR XVth world congress.
American Society of Agricultural and Biological Engineers, Chicago, Illinois
Karunakaran C, Jayas DS, White NDG (2003a) Soft X-ray inspection of wheat kernels infested
by Sitophilus oryzae. Trans ASAE 46:739–745
Karunakaran C, Jayas DS, White NDG (2003b) X-ray image analysis to detect infestations
caused by insects in grain. Cereal Chem 80:553–557
Karunakaran C, Jayas DS, White NDG (2004) Detection of internal wheat seed infestation by
Rhyzopertha dominica using X-ray imaging. J Stored Prod Res 40:507–516
Karunakaran C, Paliwal J, Jayas DS, White NDG (2005) Comparison of soft X-rays and NIR
spectroscopy to detect insect infestations in grain. In: ASAE annual international meeting.
American Society of Agricultural and Biological Engineers, Tampa, Florida
Keagy PM, Schatzki TF (1991) Effect of image resolution on insect detection in wheat radiographs. Cereal Chem 68:339–343
Keagy PM, Schatzki TF (1993) Machine recognition of weevil damage in wheat radiographs.
Cereal Chem 70:696–700
Keagy PM, Parvin B, Schatzki TF (1995) Machine recognition of naval orange worm damage in
X-ray images of pistachio nuts. In: Proceedings of SPIE—the international society for optical engineering. The Society of Photo-Optical Instrumentation Engineers, Bellingham, WA,
pp 192–203
Keagy PM, Schatzki TF, Le L, Casasent D, Weber D (1996) Expanded image database of pistachio X-ray images and classification by conventional methods. In: Proceedings of
SPIE—the international society for optical engineering. The Society of Photo-Optical
Instrumentation Engineers, Bellingham, WA, pp 196–204
Ketch S (1998) Goring X files—there's no hiding place! Food-Manuf 73:29
Kim S, Schatzki TF (2000) Apple watercore sorting system using X-ray imagery. I. Algorithm
development. Trans ASAE 43:1695–1702
Kim S, Schatzki T (2001) Detection of pinholes in almonds through X-ray imaging. Trans ASAE
44:997
Kim MS, Chen YR, Mehl PK (2001) Hyperspectral reflectance and fluorescence imaging system
for food quality and safety. Trans ASAE 44:721–729
Kirkpatrick RL, Wilbur DA (1965) The development and habits of the granary weevil, Sitophilus
granarius within the kernel of wheat. J Econ Entomol 58:979–985
Kotwaliwale N, Weckler PR, Brusewitz GH, Kranzler GA, Maness NO (2007) Non-destructive
quality determination of pecans using soft X-rays. Postharvest Biol Technol 45:372–380
Kotwaliwale N, Weckler PR, Brusewitz GH (2009) X-ray attenuation coefficients using polychromatic X-ray imaging of pecan components. Biosyst Eng 94:199–206
Kröger C, Bartle CM, West JG, Purchas RW, Devine CE (2006) Meat tenderness evaluation
using dual energy X-ray absorptiometry (DEXA). Comput Electron Agric 54:93–100
Lammertyn J, Jancsok P, Dresselaers T, Hecke PV, Wevers M, Baerdemaeker JD, Nicolaï B
(2001) X-ray CT and magnetic resonance imaging to study the development of core breakdown in ‘conference’ pears. In: ASAE annual meeting. American Society of Agricultural
and Biological Engineers, Sacramento, California
54
C. Karunakaran and D. S. Jayas
Lammertyn J, Dresselaers T, Hecke PV, Jancsok P, Wevers M, Nicolaï B (2003) Analysis of
the time course of core breakdown in ‘conference’ pears by means of MRI and X-ray CT.
Postharvest Biol Technol 29:19–28
Lenker DH, Adrian PA (1971) Use of X-rays for selecting mature lettuce heads. Trans ASAE
14(5):894
Lenker DH, Adrian PA (1980) Field model of an X-ray system for selecting mature heads of
­crisphead lettuce. Trans ASAE 23(14–19):24
Lin TT, Chang HY, Wu KW, Jiang JA, Ouyang CS (2005) An adaptive image segmentation
algorithm for X-ray quarantine inspection of selected fruits. In: ASAE annual international
meeting. American Society of Agricultural and Biological Engineers, Tampa, Florida
Lin TT, Liao YC, Huang TW, Ouyang CS, Jiang JA, Yang MM, Yang EC (2008) X-ray computed
tomography analysis of internal injuries of selected fruits. Providence, Rhode Island
Lu R, Ariana D (2002) A near-infrared sensing technique for measuring internal quality of apple
fruit. Appl Eng Agric 18:585–590
Marcoux M, Faucitano L, Pomar C (2005) The accuracy of predicting carcass composition of
three different pig genetic lines by dual-energy X-ray absorptiometry. Meat Sci 70:655–663
Mills RB, Wilbur DA (1967) Radiographic studies of Angoumois grain moth development in
wheat, corn and sorghum kernels. J Econ Entomol 60:671–677
Milner M, Lee MR, Katz R (1950) Application of X-ray technique to the detection of internal
insect infestation of grain. J Econ Entomol 43:933–935
Milner M, Lee MR, Katz R (1952) Radiography applied to grain and seeds. Food Technol
6:44–45
Morita K, Ogawa Y, Thai CN, Tanaka F (2003) Soft X-ray image analysis to detect foreign materials in foods. Food Sci Technol Res 9:137–141
Mousavi R, Miri T, Cox PW, Fryer PJ (2007) Imaging food freezing using X-ray microtomography. Int J Food Sci Technol 42:714–727
Navajas EA, Richardson RI, Fisher AV, Hyslop JJ, Ross DW, Prieto N, Simm G, Roehe R (2010)
Predicting beef carcass composition using tissue weights of a primal cut assessed by computed tomography. Anim Int J Anim Biosci 4:1810–1817
Neethirajan S, Karunakaran C, Jayas DS, White NDG (2004) X-ray CT-An emerging research
tool for food industry. In: International conference on emerging technologies in agricultural
and food engineering, vol 250. IIT KGP, India, p 255
Neethirajan S, Jayas DS, Karunakaran C (2006a) Dual energy X-ray image analysis for classifying vitreous kernels in durum wheat. In: ASABE annual international meeting. American
Society of Agricultural and Biological Engineers, Portland, Oregon
Neethirajan S, Karunakaran C, Jayas DS, White NDG (2006b) X-ray computed tomography
image analysis to explain the airflow resistance differences in grain bulks. Biosyst Eng
94:545–555
Neethirajan S, Karunakaran C, Symons S, Jayas DS (2006c) Classification of vitreousness in durum
wheat using soft X-ray and transmitted light systems. Comput Electron Agric 53:71–78
Neethirajan S, Jayas DS, White NDG (2007) Detection of sprouted wheat kernels using soft
X-ray image analysis. J Food Eng 81:509–513
Neethirajan S, Jayas DS, White NDG, Zhang H (2008) Investigation of 3D geometry of bulk
wheat and pea pores using X-ray computed tomography images. Comput Electron Agric
63:104–111
Ogawa Y, Morita K, Tanaka S, Setoguchi M, Thai CN (1998) Application of X-ray CT for detection of physical foreign materials in foods. Trans ASAE 41(1):157–162
Paliwal J, Wang W, Symons SJ, Karunakaran C (2004) Insect species and infestation level determination in stored wheat using near-infrared spectroscopy. Can Biosyst Eng 46:7.17–7.24
Pedersen JR, Brown RA (1960) X-ray microscope to study behaviour of internal infesting grain
insects. J Econ Entomol 53:678–679
Prieto N, Navajas EA, Richardson RI, Ross DW, Hyslop JJ, Simm G, Roehe R (2010) Predicting
beef cuts composition, fatty acids and meat quality characteristics by spiral computed
tomography. Meat Sci 86:770–779
Robertson JC (1976) A guide to radiation protection. Wiley, New York
3
X-ray Imaging
55
Ron PH, David CS (2002) X-ray inspection of wheat for granary weevils. realtime digital imaging
vs. film. In: ASAE annual international meeting/CIGR XVth world congress. American Society
of Agricultural and Biological Engineers, Chicago, Illinois
Sax K (1955) The effect of ionizing radiations on plant growth. Am J Bot 42:360–364
Schatzki TF, Fine TA (1988) Analysis of radiograms of wheat kernels for quality control. Cereal
Chem 65:233–239
Schatzki TF, Wong RY (1989) Detection of submilligram inclusions of heavy metals in processed
foods. Food Technol 43:72–76
Schatzki TF, Wilson EK, Kitto GB, Behrens P, Heller I (1993) Determination of hidden
Sitophilus granarius (Coleoptera: Curculionidae) in wheat by myosin ELISA. J Econ
Entomol 86:1584–1589
Schatzki TF, Young R, Haff RP, Eye JG, Wright GR (1996) Visual detection of particulates in
X-ray images of processed meat products. Opt Eng 35:2286–2291
Schatzki TF, Haff RP, Young R, Can I, Le LC, Toyofuku N (1997) Defect detection in apples by
means of X-ray imaging. Trans ASAE 40:1407–1415
Selman J (2000) The fundamentals of imaging physics and radiobiology. Charles C Thomas
Publisher Ltd, Illinois, USA
Sharifi S, Mills RB (1971a) Radiographic studies of Sitophilus zeamais Mots. in wheat kernels.
J Stored Prod Res 7:195–206
Sharifi S, Mills RB (1971b) Developmental activities and behaviour of the rice weevil inside
wheat kernels. J Econ Entomol 64:1114–1118
Sonego L, Ben-Arie R, Raynal J, Pech JC (1995) Biochemical and physical evaluation of textural
characteristics of nectarines exhibiting woolly breakdown: NMR imaging, X-ray computed
tomography and pectin composition. Postharvest Biol Technol 5:187–198
Stermer RA (1960) An X-ray device for rapid evaluation of purity of grass seed. In: ASAE (ed)
St. Joseph, MI
Stermer RA (1972) Automated X-ray inspection of grain for insect infestation. Trans ASAE
15:1081–1085
Tao Y, Ibarra JG (2000) Thickness-compensated X-ray imaging detection of bone fragments in
deboned poultry-model analysis. Trans ASAE 43(2):453–459
Tao Y, Chen Z, Jing H, Walker J (2001) Internal inspection of deboned poultry using X-ray imaging
and adaptive thresholding. Trans ASAE 44:1005–1009
Thomas P, Saxena SC, Chandra R, Rao R, Bhatia CR (1993) X-ray imaging for detecting spongy tissue,
an internal disorder in fruits of ‘Alphonso’ mango (Mangifera indica L.). J Hortic Sci 68:803–806
Thomas P, Kannan A, Degwekar VH, Ramamurthy MS (1995) Non-destructive detection of seed
weevil-infested mango fruits by X-ray imaging. Postharvest Biol Technol 5:161–165
Tollner EW (1993) X-ray technology for detecting physical quality attributes in agricultural produce. Postharvest News Inf 4:149N–155N
Tollner EW, Hung YC, Upchurch BL, Prussia SE (1992) Relating X-ray absorption to density
and water content with apples. Trans ASABE 35:1921–1928
Wilson A (2002) X-ray imaging checks food purity [Online]. http://www.vision-systems.com/
articles/print/volume-7/issue-5/features/food-inspection/x-ray-imaging-checks-food-purity.
html. Accessed 21 Apr 2013
Wort DJ (1941) X-ray effects on the growth and reproduction of wheat. Plant Physiol 18:373–383
Yuasa H (1926) On the advantage of the X-ray examination of certain classes of materials and
insects subject to the plant quarantine regulations. In: Proceedings of the third pan-pacific
science congress, p 1141
Zuech N (2001) X-ray-based machine vision—part 2: applications in industries other than
electronics [Online]. http://www.visiononline.org/vision-resources-details.cfm/vision-resources/
X-Ray-Based-Machine-Vision-Part-2-Applications-in-Industries-Other-Than-Electronics/
content_id/1300/id/2/newsType_id/0. Accessed 21 Apr 2013
Zwiggelaar R, Bull CR, Mooney MJ (1996) X-ray simulations for imaging applications in the
agricultural and food industries. J Agric Eng Res 63:161–170
Zwiggelaar R, Bull CR, Mooney MJ, Czarnes S (1997) Detection of “soft” materials by selective
energy X-ray transmission imaging and computer tomography. J Agric Eng Res 66:203–212
Chapter 4
UV Imaging
Preetam Sarkar and Ruplal Choudhary
Introduction
Ultraviolet (UV) imaging is finding increasing applications in different fields such
as food and agriculture, forensic sciences, astronomy and microscopy. In UV imaging, UV light gets absorbed on the surface of the material which enables to view
surface topology not requiring light penetration. As UV has shorter wavelengths
than visible light, it is easily scattered by the surface topology of materials which
helps to resolve or detect even smaller and finer characteristics. There are two different types of UV imaging: reflected UV imaging and fluorescence UV imaging. As
both the systems use UV light source, they are easily confused with one another. In
UV-fluorescence imaging technique, UV source of light activates the fluorescence
of a system at a longer wavelength. The fluorescent material absorbs the UV light
and then radiates energy at a longer wavelength which is a diffused emission. This
detected signal is in the visible or infrared region. In reflected UV-system, the UV
light, which is either scattered or reflected is imaged using a UV camera which can
detect in the UV region. Reflected UV-imaging technique has some disadvantages
such as exposure control, composition and focus. These problems exist due to the
fact that the UV pass filter is opaque to visible light and the human eye cannot detect
UV light through the viewfinder (Joseph 1995).
The UV band is broad because it spans from 10 to 400 nm. Two major classes
of industrial UV imaging applications exist, which are based on the UV range
within which they function. Wavelength between 300 and 400 nm is known as the
near-UV band which is further divided into UV-A (320–400) and UV-B (280–320)
P. Sarkar
Department of Food Process Engineering, National Institute of Technology,
Rourkela, Orissa, India
R. Choudhary (*)
Department of Plant, Soil and Agricultural Systems, Southern Illinois University,
Carbondale, IL 62901, USA
e-mail: choudhry@siu.edu
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_4, © Springer-Verlag Berlin Heidelberg 2014
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P. Sarkar and R. Choudhary
sub-bands. When the wavelength is below 300 nm, it is known as the deep-UV
(DUV) band, which is also called UV-C band, shortwave or germicidal UV. This
band mainly operates between 250 and 280 nm.
Reflected UV Imaging System
In reflected UV imaging, reflected light is used to photograph objects at the same
wavelength by using a specialized UV camera. Reflected UV imaging is a functional area of imaging science which has found increasing applications over the
years. This specific technique finds extensive usage in the forensic sciences as it can
detect evidences that are invisible to the human eye. Shorter UV wavelengths scatter more strongly from surface features compared to the visible or near-IR bands.
Therefore surface irregularity and scratches can be easily seen by reflected UV
images (Richards 2013). Another more important property of UV is that it is more
reflected from organic materials surfaces. Hence the reflected UV Imaging enhances
the contrast of trace organic materials from the background of inorganic materials.
Therefore, it is very useful in biological imaging including forensic investigations.
Some common examples of reflected UV applications in forensics include detection
of finger prints, bite marks, body fluids and shoe prints (Marin and Buszka 2013).
Fluorescent UV Imaging System
In fluorescent UV imaging, sample surface illumination is done using UV light,
whereas the signal is detected in visible or infrared band. The sample absorbs UV
light but radiates back at a longer wavelength. This difference between the absorbed
and emitted wavelength is called the Stoke’s Shift. Since the photons in fluorescent light are lower than the excitation light, special optics is used. Any scattered or
reflected light from object must be rejected from the fluorescence signal collection
pathway by a special series of optical filters. Detection and quantification of fluorescence is done by either PMT or CCD detectors. Most of the fluorescent imaging
systems are used in biological sciences labs to detect amino acids and nucleic acids.
Instrumentation for Reflected Imaging
UV Image Sensors
Generally classified, image sensors are of different classes. The two major types
are photoconductive and photoemissive (Joseph 1995). In photoconductive systems, photon results in the electron to transit into the conduction band. Common
examples of photoconductive devices are silicon based CCD where 1 eV energy is
required for detection. In the photoemissive system, one electron needs to be ejected
which requires few electron volts of energy. These photoemissive sensors are
4 UV Imaging
59
natural UV detectors which generates dark backgrounds at ambient temperatures.
Photoemissive detectors are very good choices for UV imaging. There are different
types of photoemissive detectors such as systems that are based on microchannel
plate (MCP) (Joseph 1995). These types of detectors have been used extensively in
X-ray observations, extreme UV emissions, and FUV/UV (when wave-length was
greater than 900 Å). An MCP system comprises of a thin disk of lead-oxide with
many microscopic channels. These channels run parallel to each other. The application of an electrical voltage changes the MCP to become an image intensifier. MCP
systems have a large surface area-volume ratio that can trap residual gases. As MCP
systems are operated at a potential which is greater than 1 keV, there remains issues
with cleanliness and conditioning.
Another type of detector, known as the ICCD or MCP-Intensified CCD operates due to multiple conversions between light and electronic signals. A third type
of UV detector commonly used is known as the electron-bombarded solid-state
arrays (EBCCD). This system has shown that it can generate a signal to noise
ratio above 100 (Joseph 1995). Silicon Carbide Geiger-mode avalanche photodiode (SiC-GM-APD) sensors are also under development and have more sensitivity
than other UV sensors reported (Shaw et al. 2009).
UV image detector is the limiting item that controls image quality. Most of the
common CCD’s or CMOS imaging systems do not image in the UV region. UV light
is not desired in monochrome or color video cameras as in one scene, both visible
and UV light cannot be focused together. This results in the creation of a purple halo
around the object of focus. In order to negate this effect, UV absorbing features are
added to CCD or CMOS to bar UV from detection. But to image in the UV region,
these features are not required (Joseph 1995). The Standard glass lenses absorb in deep
UV region, therefore, special lenses made of fused silica or calcium fluorite are used.
UV Light Sources
Traditional UV sources are fluorescent lamps typically made of mercury vapor.
Different materials for vapors and different pressures inside lamps generate different wavelength in UV region. Recently UV LEDs are commercially available.
LEDs emit monochromatic UV and therefore desirable in machine vision inspection of agricultural materials. Most common LED lightings used for fluorescent
UV applications emit at 365 nm. For reflected UV imaging, 254 nm or shorter
wavelength UV sources are used.
Applications of Imaging in Agricultural and Food
Production Systems
UV imaging is relatively new to the area of agricultural and food industry. Most of
the reported applications are still in research and development. Moreover, majority
of research applications in this area have been limited to fluorescence UV because
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P. Sarkar and R. Choudhary
Fig. 4.1 Experimental setup for the detection of potato using UV imaging systems (Al-Mallahi
et al. 2010)
Fig. 4.2 UV images of potato tubers and clods: a tuber and clod, b manually masked tubers,
c manually masked clods (Al-Mallahi et al. 2010)
of lower cost of cameras than reflected UV cameras. Al-Mallahi et al. (2010)
reported a UV machine vision system to image potato tubers on a potato harvester
(Figs. 4.1, 4.2). Their research tried to distinguish between potato clod and tubers
using the UV imaging system capturing image at 380 nm using a one CCD camera. There were lots of problems in designing such a system for potatoes due to
random size distribution of the samples, residual mud on potato surface, and random flow on conveyor belts. Their overall goal was to understand the reflectance
of potato tubers against clods when the moisture conditions were changed in the
UV range. An algorithm was created for detecting the threshold values among
tubers, clods and the conveyor surface. The results indicated that the UV imaging system was successful in detecting the tubers from the clods. They were able
4 UV Imaging
61
Fig. 4.3 The schematic diagram of the imaging system to detect aflatoxin in chili pepper (Atas
et al. 2012)
to calculate the tuber’s surface dimensions using the difference in reflectance values. The success rate for detecting tubers was 98.79 % and for clods was 98.28 %.
Presence of mud on potato surface was the primary reason for the failure of the
system in some of the cases (Al-Mallahi et al. 2010).
Hachiya et al. (2009) tried to use fluorescence UV imaging system to ­evaluate
freshness of rough rice. The overall system comprised of two UV-A ­fluorescent
lamps, a blue LED band-pass filter, CCD camera and image processing s­oftware.
Rice quality is important to be monitored after harvesting because there can be
decrease in flavor, aroma and freshness and changes in physical and chemical
properties. The study used a fluorescent imaging technique which was based on
UV-excitation mechanism. This type of system has been used previously for detection
of aflatoxins in eggs from hens and nuts. The rice quality was evaluated by capturing
an image by using a CCD camera of the fluorescence and then measurement of the
brightness of the image using a computer. The results showed that there was a high
correlation between fluorescence intensity and traditional indices for measuring of
rice quality such as free fatty acid index and guaiacol reaction index. It was observed
that the fluorescence intensity increased with increase in storage temperature and time
of storage. Also, sensory quality of the rice grains decreased significantly as the fluorescence intensity increased (Hachiya et al. 2009).
In another research, UV excitation has been used for the detection of aflatoxins
in chili pepper. This study concluded that 87.50 % classification accuracy could
be obtained using UV excitation. It was also found that 400 and 420 nm spectral
bands were the most prominent in UV excitation (Atas et al. 2012). In a similar
study on the detection of mycotoxins in chili pepper, UV and halogen illumination techniques were used (Figs. 4.3, 4.4). Neural network was used for the higher
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P. Sarkar and R. Choudhary
Fig. 4.4 Sample images of the uncontaminated and contaminated pepper for the halogen and
UV illuminations (Atas et al. 2012)
discrimination of spectral bands. A quantized histogram matrix was used for feature extraction. This study showed that by using halogen excitation, a classification
accuracy of 91 % could be achieved (Atas et al. 2011).
Momin et al. (2011) used a microprocessor based spectrophotometer for measuring absorption spectrum of selected varieties of citrus fruits. The level of fluorescence was measured using a machine vision system. It was concluded that UV
lamps between 340 and 380 nm provided the best fluorescent image. Based on
this, an image device was developed which was made up of three lighting panels using UV LED (365 nm), black and blue lamp at 350 nm and UB-B lamp at
306 nm. Images from this set of experiment also correlated well from the previous
sets indicating that this technique is robust for detecting damaged or injured citrus
fruits (Momin et al. 2011).
Yang et al. (2010) developed UV-A excitation and fluorescence imaging system
for fecal contamination of leafy vegetables. The wavelength of excitation ranged
between 320 and 400 nm. The study was done for detection of bovine fecal contaminants on the axes of lettuce and baby spinach leaves. Correlational analysis
was used to select the wavebands at 666 and 680 nm. This study concluded that
the system could accurately detect the majority of fecal contaminations on the
leaves (Yang et al. 2010). UV excitation and imaging system has also been studied
for detection of mycotoxins in food samples. Yao et al. (2010) reported detection
of aflatoxin on single corn kernel using hyperspectral fluorescence based system
based on long wavelength UV excitation. Different classification algorithms such
as maximum likelihood and binary encoding were used for classification of the
corn kernels. It was concluded that the binary encoding method showed higher
image qualities at 87 and 88 % (20 and 100 ppb were the classification threshold).
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63
Hyperspectral fluorescence based imaging system with UV-C excitation of
pathogenic biofilm has been reported recently for application in food industry. Jun
et al. (2009) used hyperspectral fluorescence based imaging system for the detection of two different genera of biofilms on food contact surface (stainless steel).
UV-A excitation between 416 and 700 nm was used for acquiring the images.
Threshold method at 480 nm showed that Salmonella produced more intense biofilms when compared with Escherichia coli O157:H7 (Jun et al. 2009). In a similar
study, the same research group (Jun et al. 2010) reported detection of biofilm on
five different food contact surfaces namely stainless steel, high-density polyethylene (HDPE), plastic laminate (Formica), and two types of polished granite. This
research aimed at understanding the minimum number of spectral bands that can
be used for detection of the biofilms on food contact surfaces. UV-A excitation
ranging between 421 and 700 nm were used in the study. It was found that the biofilms could be detected at a detection rate of about 95 % (Jun et al. 2010).
Other Applications of Reflected Imaging
Although reflected UV imaging has been used in astronomy for over a century, its
application in other areas are still under development. A second major application
has been reported in forensic science for detection of finger prints and body fluids
from criminals and victims. However, the intention of this paper is to review areas
other than the traditional areas of forensic and astronomy. Most recently reflected
UV imaging systems are commercially available for scanning of drugs and chemicals in microfluidics. Such devices are very useful in pharmaceutical industry for
measuring drug dissolution rate to estimate bioavailability of drugs. One of such
commercial reflected UV imager for pharmaceutical industry, ActiPix SDI3000
has been used in several research projects recently by a research group involved in
drug discovery (Ye et al. 2011, 2012a, b).
Ye et al. (2011) used UV imaging system for the real-time analysis of drug
diffusion from hydrogel based delivery systems. Pluronic 127, a non-ionic surfactant and a triblock copolymer was used as the hydrogel system. Three different
levels of polymer concentrations on the drug (piroxicam) diffusion was studied.
It was confirmed using small-angle X-ray scattering (SAXS) that as F127 concentrations increased, there was a steady decline in the diffusion kinetics of the drug
molecule. Drug release from 30 % (w/w) F127 gel was studied using UV imaging
system. The system could provide data regarding gel dissolution rate, thickness
alterations in the boundary layer and the release characteristics of the drug.
Ye et al. (2012a) reported use of the above UV imaging method for quantification
of diffusion coefficient and real-time distribution pattern of the drug molecule (piroxicam) encapsulated within hydrogels. Hydrogel based systems have found increasing
applications in the rational design of drug delivery systems and in tissue engineering as they are structurally similar to biological tissues. Pluronic F-127 hydrogel
has found lots of applications in controlled release of drug molecules. The study
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P. Sarkar and R. Choudhary
concluded that UV imaging provided vivid description about the real-time spatial
distribution of the drug around the site of injection. The same research group published the concentration maps of piroxicam using the UV imaging system. Results
suggested that UV imaging could potentially monitor the transport characteristics of
the drug meant for subcutaneous applications (Ye et al. 2012b).
Sarnes et al. (2013) reported using UV imaging technique for observing the drug
dissolution pattern of an active molecule, indomethacin. The major goal was to
understand the local concentration differences and supersaturation conditions of the
poorly soluble drug. The UV imaging results demonstrated that the drug showed
powerful signals when the flow-through dissolution system was started. This similar
observation was seen when the wavelength was increased from 265 to 550 nm.
Gaunø et al. (2013) published in vitro release characteristics of 5-aminosalicyclic
acid from single extrudates using UV imaging technique. Ethyl cellulose was used
to coat 5-aminosalicyclic extrudates using lab coater. UV imaging for 240 min was
used to understand the release profile of the drug from extrudates which were coated
with four different levels. UV imaging confirmed that the release pattern of the drug
was in harmony with dissolution test data.
Pajander et al. (2012) aimed at understanding the rheological and physical behavior of hydropropyl methylcellulose (HPMC) in solution using UV imaging technique. UV imaging method was used to understand the behavior of the polymer at
the surface of the compact. Rheological parameters such as steady shear and oscillatory shear were studied using a rheometer. UV imaging could monitor three different
phases of HPMC in solution, namely, gel formation, expansion of gel into solution
stage and steady state conformation. The study demonstrated that UV imaging can
be used successfully for monitoring of polymer properties in solution systems.
In recent study by Hulse et al. (2012) the dissolution behavior such as intrinsic
dissolution rate of three drugs, namely, indomethacin, theophylline and ibuprofen
were studied using UV imaging (flow-through dissolution technique). The study
demonstrated that the intrinsic dissolution rate ratio between theophylline anhydrate to theophylline was 2.1. The study also confirmed that UV imaging method
can be successfully used to capture dissolution pattern of such drug molecules.
Most recently Kern et al. (2013) used UV cameras to monitor the sulfur dioxide
distribution in space in volcanoes. Two different aspects were modeled, namely
UV transmittance based on wavelength and UV radiation. The study demonstrated
that UV imaging sensitivity changed from the Lambert-Beer law’s approximations. It was also found that as the distance increased between sulfur dioxide production site and the UV camera, the sensitivity of detection reduced significantly.
Conclusions
Reflected UV imaging has traditionally been used in astronomy and forensic science. Fluorescent UV imaging has been traditionally used in molecular biology. The
application of UV imaging in agricultural and food industry is an emerging area of
4 UV Imaging
65
research. It has not been used by this industry except for a few research trials that
too mostly in UV excitation and fluorescence imaging because of lack of suitable
UV detectors available to the food processors at affordable prices. Reflected imaging
in UV region allows viewing features and characters not readily observed in X-ray
or infrared imaging. Thus the UV imaging can empower the agricultural and food
industry with a new tool to detect defects and contaminations to ensure food safety
and quality. There is a great potential for application of UV imaging in food defense
and safety and therefore with the evolution of new generation of UV detectors and
cameras, more research and application of UV imaging in food industry will follow.
References
Al-Mallahi A, Kataoka T, Okamoto H, Shibata Y (2010) Detection of potato tubers using an
ultraviolet imaging-based machine vision system. Biosyst Eng 105(2):257–265
Atas M, Yardimci Y, Temizel A (2011) Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning, In: Proceedings of SPIE—the international society
for optical engineering, p 8027
Atas M, Yardimci Y, Temizel A (2012) A new approach to aflatoxin detection in chili pepper by
machine vision. Comput Electron Agric 87:129–141
Gauno MH, Vilhelmsen T, Larsen CC, Boetker JP, Wittendorff J, Rantanen J, Østergaard J (2013)
Real-time in vitro dissolution of 5-aminosalicylic acid from single ethyl cellulose coated
extrudates studied by UV imaging. J Pharm Biomed Anal 83:49–56
Hachiya M, Asanome N, Goto T, Noda T (2009) Fluorescence imaging with UV-excitation for
evaluating freshness of rice. Japan Agric Res Quaterly 43(3):193–198
Hulse WL, Gray J, Forbes RT (2012) A discriminatory intrinsic dissolution study using UV area
imaging analysis to gain additional insights into the dissolution behaviour of active pharmaceutical ingredients. Int J Pharm 434:133–139
Joseph CL (1995) UV image sensors and associated technologies. Exp Astron 6:97–127
Jun W, Kim MS, Lee K, Millner P, Chao K (2009) Assessment of bacterial biofilm on stainless
steel by hyperspectral fluorescence imaging. Sens Instrum Food Qual Saf 3:41–48
Jun W, Kim MS, Cho B-K, Millner PD, Chao K, Chan DE (2010) Microbial biofilm detection on
food contact surfaces by macro-scale fluorescence imaging. J Food Eng 99(3):314–322
Kern C, Werner C, Elias T, Sutton AJ, Lubcke P (2013) Applying UV cameras for SO2 detection
to distant or optically thick volcanic plumes. J Volcanol Geoth Res 262:80–89
Marin N, Buszka J (2013) UV and narrowband visible light imaging. In: Alternative light source
imaging, p 25–61
Momin MdA, Kondo N, Kuramoto M, Ogawa Y, Shigi T (2011) Study on excitation and fluorescence spectrums of Japanese citruses to construct machine vision systems for acquiring fluorescent images. In: Proceedings of SPIE—the international society for optical engineering, p 8027
Pajander J, Baldursdottir S, Rantanen J, Østergaard J (2012) Behaviour of HPMC compacts
investigated using UV-imaging. Int J Pharm 47:345–353
Richards A (2013) UV Imaging opens new applications. Vision systems design. Available online at:
h t t p : / / w w w. v i s i o n - s y s t e m s . c o m / a r t i c l e s / p r i n t / vo l u m e - 1 1 / i s s u e - 7 / f e a t u r e s /
component-integration/uv-imaging-opens-new-applications.html
Sarnes A, Østergaard J, Jensen SS, Aaltoten J, Rantanen J, Hirvonen J, Peltonen A (2013)
Dissolution study of nanocrystal powders of a poorly soluble drug by UV imaging and channel flow methods. Eur J Pharm Sci 50:511–519
Shaw GA, Siegel AM, Model J, Geboff A, Soloviev S, Vert A, Sandvik P (2009) Deep UV photon-counting detectors and applications. In: Itzler MA, Campbell JC (eds) Advanced photon counting techniques III. SPIE—the international society for optical engineering, Orlando FL, USA, p 73200 J-15
66
P. Sarkar and R. Choudhary
Yang C-C, Jun W, Kim MS, Chao K, Kang S, Chan DE, Lefcourt A (2010) Classification of fecal
contamination on leafy greens by hyperspectral imaging. In: Proceedings of SPIE—the international society for optical engineering, p 7676
Yao H, Hruska Z, Kincaid R, Ononye A, Brown RL, Cleveland TE (2010) Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image. In: Proceedings of
SPIE—the international society for optical engineering, 7676
Ye F, Larsen SW, Yaghmur A, Jensen H, Larsen C, Østergaard J (2011) Real-time UV imaging of
drug diffusion and release from Pluronic F127 hydrogels. Eur J Pharm Sci 43:236–243
Ye F, Larsen SW, Yaghmur A, Jensen H, Larsen C, Østergaard J (2012a) Drug release into hydrogel-based subcutaneous surrogates studied by UV imaging. J Pharm Biomed Anal 71:27–34
Ye F, Larsen SW, Yaghmur A, Jensen H, Larsen C, Østergaard J (2012b) Real-time UV imaging
of piroxicam diffusion and distribution from oil solutions into gels mimicking the subcutaneous matrix. Eur J Pharm Sci 46:72–78
Chapter 5
Visible Light Imaging
Neetha Udayakumar
Introduction
The visible light is that region of the electromagnetic spectrum that is detectable
by the human eye, whose wavelength ranges from 400 to 700 nm. This region is
located in between ultraviolet (UV) and infrared (IR) regions (Fig. 5.1).
The root dates back to 1665, when Sir Isaac Newton described the concept of
dispersion of light after passing it through a prism and observed the splitting of
light into colors (ElMasry and Sun 2010).
When light falls on an object, it is normally reflected, absorbed, or transmitted.
The reflected light bounces off the object surface, transmitted light passes through
the object, and the absorbed light forms that part of energy that is absorbed within
the material (Jha 2010). The intensity to which these phenomena take place
depends on the nature of the material and that specific wavelength region of the
electromagnetic spectrum that is being used (Jha 2010).
The region of frequency consisting of extremely small range of wavelengths and
that can be sensed by human eyes and other organisms is called the visible light
or spectrum. This region is a combination of red, orange, yellow, green, blue, and
violet waves. Each color is represented by a specific wavelength, where violet is in
the area of 400 nm, blue and green are seen in the middle of the visible spectrum,
and red is in the area of 700 nm (Robertson 2012). The eyes are most sensitive to
yellow-green light (of about 550 nm), under moderate-to-strong illumination conditions (Jha 2010). In imaging systems, light plays a significant role in order to see
clearer, farther, and deeper and to gain detailed information about different objects
under investigation (ElMasry and Sun 2010). The visible light, emitted, transmitted,
or reflected from a sample, carries information about that sample that facilitates the
N. Udayakumar (*)
School of Biosystems Engineering, University College Dublin,
Belfield, Dublin 4, Ireland
e-mail: neetha.au@gmail.com
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_5, © Springer-Verlag Berlin Heidelberg 2014
67
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N. Udayakumar
Fig. 5.1 The electromagnetic spectrum (ElMasry and Sun 2010)
consumers and quality inspectors to get information regarding the quality. But only a
very limited region is within the range of human vision, whereas other wavelengths
that carry the information may be far beyond the range of human vision. Visible light
imaging is also called RGB (red, green, and blue) color imaging, as the images of
samples appear colored by these primary colors red, green, and blue (RGB system).
It is a conventional color imaging technique, where an image is represented at
any given point (pixel) as the intensity of these three base colors red, green, and
blue (Gunasekaran 1996).
Instrumentation and Hardware
The hardware configuration of the image acquisition system (Fig. 5.2) generally
consists of an illumination device, a solid-state charge-coupled device (CCD)
array camera, a frame grabber, a personal computer with a suitable software for
image processing and interpreting results, and a high-resolution color monitor
(Wu and Sun 2013).
Illumination
The captured image basically depends on the quality of illumination. A welldesigned illumination system can not only improve the accuracy, but can also reduce
the time and complexity of the subsequent image processing steps, leading to a successful image analysis, and decrease the cost of an image processing system (Du
and Sun 2004; Gunasekaran 1996). It is also important to have a good lighting
system, so as to reduce reflection, shadow, and some noises, thereby enhancing an
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69
Fig. 5.2 Image acquisition
system (Leon et al. 2006)
image quality (Sun 2000). The two widely used illuminants are the fluorescent and
incandescent bulbs, but there are also some other useful light sources, such as lightemitting diodes (LEDs) and electroluminescent sources (Wu and Sun 2013).
Cameras
A digital camera with a minimum resolution of 1600 × 1200 pixels is recommended for imaging, which is equivalent to a 2.1-megapixel or higher camera
(Yam and Papadakis 2004). The digital camera records images on an electric light
sensor that is made up of millions of tiny points or pixels; there are two major factors that affect the quality of the image—resolution and file compression (Yam and
Papadakis 2004). Resolution is related to the number of pixels on the light sensor: the more the pixels, the higher the resolution and the better the image quality.
File compression reduces the amount of memory taken up by the image and allows
more images to be stored (Yam and papadakis 2004).
The two major types of digital cameras used in visible light imaging are the
CCD and CMOS (complimentary metal–oxide–semiconductor), both being solidstate imaging camera used here to convert photons to electrical signals. An imaging camera receives light from the surface of an object and converts the light into
electrical signals using a CCD. The CCD in the camera could be of two types,
namely a single-chip CCD camera and a three-chip CCD camera. The single-chip
CCD camera consists of a Bayer sensor, which is commonly used for capturing
digital color images (Wu and Sun 2013). Bayer sensors and three-chip CCD cameras differ from each other by the way of color separation. Single-chip CCD cameras use a color filter array consisting of many squares, where each square contains
four pixels with one red filter, one blue filter, and two green filters, because human
eye is more sensitive to the green of the visible spectrum and less sensitive to red
and blue. The missing color can be inserted using a demosaicing algorithm. Threechip CCD cameras have three discrete image sensors, which help in bringing about
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N. Udayakumar
better color separation, and a dichroic beam splitter prism that splits the light into
red, green, and blue components, and each sensor in three-chip CCD cameras
responds to one of the three colors (Wu and Sun 2013). A CCD camera consists
of several photodiodes, known as pixels, that are made of light-sensitive materials.
They are used to read out light energy falling on it as an electronic charge (Wu and
Sun 2013). Each CCD in a three-chip camera receives RGB colors to produce near
true color images of the objects (Chen et al. 2002).
Frame Grabber
Frame grabbers were used to provide the functions of digitization, synchronization, data formatting, local storage, and data transfer from the camera to the computer to generate a bitmap image, during the times when only analog cameras
were available (Wu and Sun 2013). The camera, along with the frame grabber, is
used for acquiring images. The frame grabber can acquire either digital or analog
image, depending on the camera used (Chen et al. 2002). But nowadays digital
cameras do not need frame grabbers for digitization (Wu and Sun 2013).
Color Models
The purpose of a color model (also known as color space or color system) is to
facilitate the specification of colors in some standard, generally accepted way
(Gonzalez and Woods 2007). Color space is a mathematical representation, used
for associating tristimulus values with each color, and generally, there are three
types of color spaces, namely hardware-orientated space, human-orientated space,
and instrumental space (Wu and Sun 2013). Hardware-oriented spaces are meant
for hardware processing, image acquisition, storage, and display (Wu and Sun
2013). They can sense even a very small amount of color variation, hence making
it popular for evaluating color changes in food products during processing (Lana
et al. 2005). Human-oriented spaces correspond to the concepts of tint, shade, and
tone, which an artist defines, based on the intuitive color characteristics, whereas
instrumental spaces are used for color instruments (Wu and Sun 2013). The various color models are as follows: the RGB color model, CMY and CMYK color
models, HSI color model, and L*a*b* color models, the most popular being the
RGB model. The color models are described in detail below.
The RGB Color Model
RGB model is the most often used conventional color model, wherein each sensor captures the intensity of light in the red (R), green (G), or blue (B) spectrum,
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71
Fig. 5.3 The RGB (red,
green, and blue) color space
model (Gunasekaran 1996)
respectively (Leon et al. 2006). RGB space is defined by coordinates on three
axes, i.e., red, green, and blue (Fig. 5.3). It is the way in which cameras sense
natural scenes and display phosphors work (Russ 1999). When all three color values are zero, the object color is black, and when they all are maximum, it is white
(Gunasekaran 1996). In a typical machine vision system, each of these base color
intensities is represented by 8 bits of resolution. In the RGB color model, an image
is represented at any given point (pixel) as the intensity of these three basic colors
(Gunasekaran 1996). The models used in practice are the RGB (red, green, and
blue) model for color monitors and a broad class of color video cameras.
The CMY and CMYK Color Models
Cyan, magenta, and yellow are the secondary colors of light; as white light
strikes translucent inks, certain visible wavelengths are absorbed, while others are
reflected to the eyes. The three colors (cyan, magenta, and yellow) are used to create other colors (Fig. 5.4). In theory, these three colors should combine to absorb
all the light and produce the black color; however, a muddy brown is produced
instead, because all printing inks contain some impurities. Thus, the fourth primary ink color (black) is needed to produce a true black.
CMYK is another popular hardware-oriented space, mainly used for television
transmission, printing and copying output and so on, hence not being used in the
food industry (Wu and Sun 2013). The CMYK model is also device dependent and
is used in four-color process printing (Yam and Papadakis 2004). Most devices
that deposit colored pigments on paper, such as color printers and copiers, require
CMY data input or perform an RGB-to-CMY conversion internally (Gonzalez and
Woods 2007).
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Fig. 5.4 The CMY color
model (Wu and Sun 2013)
The HSI Color Model
The HSI (hue, saturation, and intensity) model is one that corresponds closely
with the way humans describe and interpret color (Gonzalez and Woods 2007).
The HSI model (Fig. 5.5) falls under the human-oriented spaces. HSI stands for
hue, saturation, and intensity. Hue is defined as the attribute of a visual sensation,
according to which an area appears to be similar to one of the perceived colors,
red, yellow, green, and blue, or to a combination of two of them. Saturation is
defined as the colorfulness of an area judged in proportion to its brightness. On
the other hand, brightness is defined as the attribute of a visual sensation according
to which an area appears to emit, and lightness is defined as the brightness of an
area judged relative to the brightness of a similarly illuminated area that appears to
be white or highly transmitting (Fairchild 2005). Since this color model is developed based on the concept of visual perception in human eyes, their color measurements are user-friendly and have a better relationship to the visual significance
of food surfaces (Wu and Sun 2013).
The L*a*b* Color Model
The L*a*b* model (Fig. 5.6) is an international standard for color measurement,
developed by the Commission Internationale d’Eclairage (CIE) in 1976 (Yam and
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Fig. 5.5 The HSI color
model (Wu and Sun 2013)
Fig. 5.6 The L*a*b* color
model (Wu and Sun 2013)
Papadakis 2004). It consists of a luminance or lightness component (L* value,
ranging from 0 to 100), along with two chromatic components (ranging from
−120 to +120): the a* component (from green to red) and the b* component
(from blue to yellow). The L*a*b* color model is device independent, providing
consistent color, regardless of the input or output device such as digital camera,
scanner, monitor, and printer. The L*a*b* values are often used in food research
studies (Yam and Papadakis 2004).
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Imaging with RGB Color Camera
Conventional imaging has become a very significant tool for assessing the quality
of food products, in food industry applications. Food products are analyzed for
assuring their quality, with the aid of machines. RGB color imaging is a promising technique currently applied for food color measurement, with the ability of
providing a detailed characterization of color uniformity at pixel-based level (Wu
and Sun 2013). The core concept of the technology involves image processing and
analysis which can classify and quantify objects. A basic machine vision system
comprises of a camera, a computer equipped with an image acquisition board,
and a lighting system (Chen et al. 2002). The machine captures the images of the
sample, and then, those acquired images are processed and used by the inspectors
for investigation. In order for the images to be analyzed and processed, computer
hardware and software are used. The inspectors can detect the presence of any foreign body or damage in the product. The whole concept of image analysis enables
rapid signal processing, and machine vision systems have been developed to successfully carry out scanning and sorting of millions of items per minute (Lou and
Nakai 2001). It uses image processing routines and has been an alternative integral
part of the industry’s move toward automation (ElMasry and Sun 2010).
The principal steps in image processing analysis in RGB color imaging are
mentioned below (Dowlati et al. 2012).
Image Acquisition
The very first step in RGB color imaging is image acquisition, and the quality of
data during acquiring the images is the main concern; therefore, very important
aspects such as consistent sample preparation, noise reduction, consistent illumination and reduction in specular reflection, and correct acquisition equipment are
all essential in order to produce precise images, fine enough to see the required
details and proceed them for rapid image processing (Jackman et al. 2011). Image
acquisition converts a video analog image into its digital form so that subsequent
processing can be carried out (Sun 2000). Increased image quality can reduce the
time and complexity of the subsequent processing step and enhances the acquisition of useful information (Du and Sun 2004). The common equipments used
for image acquisition in food applications are the CCD camera, magnetic resonance imaging (MRI), ultrasound (US), computed tomography (CT), and electrical
tomography (ET) (Du and Sun 2004).
Image Processing
Image acquisition is followed by image processing. This is done in order to extract
the features and analyze them. Image processing are of three levels—pre-processing,
which improves the quality of the image by noise removal and transforms the
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75
data into more convenient formats, intermediate processing, which involves the
segmentation of the region of interest from the image, and high-level processing,
which involves the description of the region of interest and to build a predictive
model from the extracted features (Jackman et al. 2011).
Segmentation
Segmentation involves the removal of the background from the object. But care
should be taken, so as to remove the non-useful subregions alone, which may be
difficult (Jackman et al. 2011). Segmentation (Fig. 5.7) is a critical step in image
processing, since the extraction of image information highly depends on the segmentation results. The goal of image segmentation is to divide an image into
regions that have a strong correlation with objects or areas of interest (Sun 2000).
Thresholding (Fig. 5.7), which is gradient based, region based (Fig. 5.8), and classification-based, is the main type of segmentation algorithm found in food quality
applications (Du and Sun 2004).
Classification to Identify the Class Groups of Objects
Classification identifies objects by classifying them into one of the finite sets of
classes, wherein the measured feature of a new object is compared to that of a
known object, or other known criteria, and determining whether the new object
belongs to a particular category of objects (Du and Sun 2008). Certain approaches
have been taken to perform this task, artificial neural network (ANN) and statistical approaches being the two main methods used for classification (Sun 2008).
Advantages and Limitations of RGB Color
Imaging Systems
Automation increases productivity and changes the quality of work, minimizing
human efforts, yielding more accuracy, cost-effectiveness, high speed, and less
strain on humans. Imaging system is combined with an illumination system, typically where the personal computer is connected with electrical and mechanical
devices that can minimize the human effort in performing a given task (Du and
Sun 2006). Image processing and analysis form the basis for RGB color imaging
technology. Certain advantages of the technology include the following: This system has a great ability in working on several objects per second instead of several seconds per object (ElMasry and Sun 2010). Evaluation of food quality using
color imaging can also reduce production costs (Sun and Brosnan 2003). Precise
descriptive data are generated (Sapirstein 1995), and quick, easy and consistent
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N. Udayakumar
Fig. 5.7 Threshold-based
segmentation: a original
image and b segmented
image (Zheng et al. 2006a, b)
Fig. 5.8 Region-based
segmentation (Narendra
and Hareesh 2010)
(Gerrard et al. 1996), robust (Gunasekaran and Ding 1993) and permanent record
is created, allowing further analysis later (Tarbell and Reid 1991). It is a nondestructive and a real-time method. This makes the technology much preferred
5 Visible Light Imaging
77
over others. However, there are some limitations of this technology. It is ineffective
when it comes to objects of similar colors, performing complex classifications
and its inability to predict chemical composition (ElMasry and Sun 2010). Object
identification is considerably more difficult in unstructured scenes (Shearer and
Holmes 1990), and artificial lighting is required in dim or dark conditions (Stone
and Kranzler 1992). Physical attributes such as color, shape, texture, and size
can be evaluated easily by the use of ordinary RGB camera, but internal structures are difficult to detect by those simple and conventional means (Du and Sun
2004). The capability of RGB imaging in the food industry has long been recognized (Tillett 1990). A fairly good amount of research has been carried out, which
has highlighted the potential of imaging system for the inspection, grading, and
quality analysis of different food types such as fruits, vegetables, meat and fish,
cheese, pizza, and bread (Brosnan and Sun 2004). Both objective and non-destructive assessments of visual quality characteristics in food products are facilitated
(Timmermans 1998).
Applications of RGB Color Imaging
Visible light, or conventional imaging, is widely used in industries, such as agriculture, pharmaceuticals, food, textiles, cosmetics, and polymer production, for its
high speed, low cost, and non-destructive analysis ability (Yan et al. 2005). The
pre-harvest, post-harvest, and food industry applications of RGB color imaging
are discussed below.
Pre-harvest Applications
The number of fruits (ripe and unripe) on a tree has been counted using image
analysis, prior to harvesting (Narendra and Hareesh 2010). The use of imaging technology for the location of stem/root joint in carrot has also been assessed
(Batchelor 1989). “The crop yield of mango (Fig. 5.9) was estimated using RGB
color imaging using the fruit at stone-hardening stage and nighttime imaging”
(Payne et al. 2013). RGB color images acquired in orchards under natural illumination were used to determine the number of green apples (Fig. 5.10) grown there
(Linker et al. 2012).
Post-harvest Applications
The potential of RGB color imaging was tested by Novell et al. 2012 on apples,
to grade them and discriminate their maturity levels under different storage conditions while going along their shelf life. Nagata et al. (1997) investigated the use
78
N. Udayakumar
Fig. 5.9 Estimation of
mango using RGB color
imaging (Payne et al. 2013)
Fig. 5.10 RGB color
imaging used to determine
the number of green apples
(Linker et al. 2012)
of color imaging to sort fresh strawberries, based on their size and shape. Feature
extraction and pattern recognition techniques were developed by Howarth and
Searcy (1992) to characterize and classify carrots for forking, surface defects, curvature, and brokenness.
Liu (1997) developed a digital image analysis method for measuring the
degree of milling of rice. Irregular potatoes (Fig. 5.11) were sorted in line using
RGB color imaging technique (ElMasry et al. 2012). The technique was also
applied for the automated inspection and grading of mushrooms (Heinemann et al.
1994), as their discoloration is undesirable, which makes it lose its market value.
Strawberries were sorted out, on the basis of their shape and size, with the help of
RGB color imaging (Nagata et al. 1997). Defect inspection in asparagus was carried out using RGB color imaging (Rigney et al. 1992). Sorting of 3.5 million fruit
in 8 h in a day, with the help of RGB color imaging, was described by Tao et al.
(1995). The imaging system has also been used for the classification of shape,
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79
Fig. 5.11 The first row
shows original images of
some irregular potatoes
moving on the conveyor belt,
and the second row is the
segmentation of tubers from
the background (ElMasry
et al. 2012)
detection of defects, quality grading, and variety of classifications of fruits such as
apples, oranges, strawberries, raisins, and others. Bruised and non-bruised regions
on Golden Delicious apples were detected using the same technology (Throop et al.
1993). Ripeness estimation of grape berries and seeds was performed using image
analysis (Rodriguez-Pulido et al. 2012).
Food Industry
The feasibility of using visible spectroscopy was investigated to assess the soluble
solid content and the pH of rice wines (Liu et al. 2007). Classification of Spanish
and Australian Tempranillo wines was carried out using visible spectroscopy (Liu
et al. 2006). Visible spectroscopy combined with backpropagation neural network
(BPNN) and least-squares support vector machine (LS-SVM) was examined to
implement the rapid discrimination of instant milk teas (Liu et al. 2009). Visible
light has been useful for various purposes in the food industry, such as for processing and packaging. The transmission of visible light has been an important parameter in designing the right packaging for foods, in order to preserve and protect
the products until they reach the consumer (Goncalves et al. 2011). Visible light
transmission can be used for many kinds of drinks, and most foods are opaque to
visible wavelengths (Tothill 2003).
Color imaging of food products is operated at visible wavelengths and used in
the analysis of foods for their quality aspects. It is an application of the machine
vision system, and these systems replace human inspectors for the quality evaluation of foods. It is a simple and affordable method, not too technologically
demanding.
Mainly, RGB color imaging is used in food industries for quality assurance
purposes, in order to replace manual grading and make the task automated. It has
become a much-needed online measurement tool. At present, the imaging applications range from vision-guided robot assembly to inspection tasks (Gunasekaran
1996).
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N. Udayakumar
Baking Industry
The technology has been applied to bakery products as well, where the appearance, texture, and flavor play a very important role. A system was described in
which the defects in baked loaves of bread were measured, by analyzing its height
and slope (Scott 1994). Bread and cake were also examined for its internal structure, using RGB color imaging (Sapirstein 1995). In a more recent study, chocolate chip cookies were examined, and the digital images acquired from them
were used to estimate the physical features such as size, shape, and dough color
(Davidson et al. 2001). Muffins were visually inspected to statistically classify
them on the basis of their surface color (Abdullah et al. 2000).
Meat, Fish, and Poultry
RGB color imaging has been a promising technique for predicting the color of meat
(Mancini and Hunt 2005). The feasibility of using image-based beef grading was
investigated (McDonald and Chen 1990). Fat was discriminated from lean, based
on reflectance characteristics, and poor results were reported. RGB imaging has also
been of help to carry out the analysis of pork loin chop images (Lu et al. 2000). Pork
color was evaluated using RGB color imaging (Lu et al. 2000). A technique was
investigated for the characterization of spectral image of poultry carcasses for separating the bruised, tumorous, and skin-torn carcasses from the normal ones (Park et al.
1996). Fat content in poultry was estimated, using RGB imaging technology (Chmiel
et al. 2011). It was also used for meat color measurement (Girolami et al. 2013). In
earlier years, machine vision was being used to detect color changes in beef ribeye
steaks during cooking (Unklesbay et al. 1986). The muscle color of beef ribeye steaks
was also determined using the same (Gerrard et al. 1996). RGB color imaging was
used to predict sensory color responses in beef (Tan et al. 1999). The color of large
cooked beef joints was correlated with its moisture content, using RGB color imaging system (Zheng et al. 2006a, b). It was also used for estimating the shrinkage of
large cooked beef joints during air blast cooling (Zheng et al. 2006a, b). This imaging
system is very frequently used for the identification of carcass ailments, its grade, or
contaminants (Jackman et al. 2011). This technique was also applied for the online
monitoring of shrimp color changes during drying. In case of fish, their fat, bones,
and skin are known to give some useful information, and RGB imaging technology
has been able to successfully predict the breed, species, quality, and gender of the fish
(Jackman et al. 2011). Different breeds of clam have been identified using this technology (Costa et al. 2010). An RGB color imaging system was successfully used for
the identification of centerline of cod fillets with a small error (Sivertsen et al. 2009).
Trout was investigated to see whether its selective breeding could be supplemented
using this technology to measure the flesh features (Kause et al. 2008). RGB imaging
was used to estimate the cod fecundity (Klibansky and Juanes 2008). It was also used
to detect the red skin defect of raw hams (Ulrici et al. 2012).
5 Visible Light Imaging
81
Fruits and Vegetable Industry
RGB color imaging has been proved to be useful in the vegetable industry as well, to
meet the increased requirements (Shearer and Payne 1990). Quality inspection of beans
was carried out on the basis of size and color quantification of samples (Kilic et al.
2007). Cabbage head was recognized using image processing algorithms and for estimating the head size (Hayashi et al. 1998). Carrots were classified for surface defects,
brokenness, and curvature (Howarth and Searcy 1992). A lot of research has been carried out on fruits too. Mostly, it was found that the application was used for apples.
RGB imaging system was used to evaluate sugar and acid content of Iyokan orange
(Kondo et al. 2000). Measurement of banana color, as compared to a colorimeter, was
investigated (Mendoza and Aguilera 2004). The quality of blueberries was assessed
using RGB color imaging (Matiacevich et al. 2011). Viscoelastic characteristics of date
fruits were determined with the help of the technology (Alirezaei et al. 2013). RGB
imaging was used to assess the feasibility of conducting color rating of sweet cherries
in outdoor orchard environments (Wang et al. 2012). A relationship between visual
appearance and browning as evaluated by image analysis and chemical traits in freshcut nectarines was developed using RGB imaging technology (Pace et al. 2011).
The potential of this technology was evaluated to determine the phenolic maturity
stage of grape seeds (Rodriguez-Pulido et al. 2012). RGB color imaging was applied
for the detection of early split in pistachio nuts (Pearson and Slaughter 1996).
Prepared Foods
RGB color imaging technology has been used for the quality assessment of
prepared foods.
An image analysis system was developed to determine the appearance and
color of oriental noodles (Hatcher et al. 2004). The color measurement of potato
chips, as compared to two colorimeters, was determined using this imaging technology (Scanlon et al. 1994).
Using RGB color imaging technology, the meltability and browning properties
of different sizes of Cheddar and Mozzarella cheese samples were investigated
under different cooking conditions (Wang and Sun 2002a, b). The functional properties of Cheddar cheese were evaluated using RGB color imaging technology
(Wang and Sun 2001). It was also used for the quality classification of corn tortillas (Mery et al. 2010). Melting property of cheese was also evaluated (Wang and
Sun 2002a, b). RGB imaging was used for the inspection of pizza topping percentage and its distribution (Sun 2000). Acrylamide concentrations were estimated in
potato chips and French fries, using RGB image analysis (Gokmen et al. 2007). An
appearance-based descriptive sensory evaluation of meals was carried out using this
system (Munkevik et al. 2007). The influence of sprout damage on the appearance
of noodles was evaluated (Hatcher and Symons 2000). The technology was also
applied for the estimation of sensory properties of sausage (Loannou et al. 2002).
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N. Udayakumar
Liquids
A relationship between computer vision and sensory evaluation of the color attributes in orange juices was explored using RGB color imaging (Fernandez-Vazquez
et al. 2011). The color appearance of red wines was measured using a calibrated
computer vision camera for various wines with reference to change in depth
(Martin et al. 2007). RGB imaging was used to determine the beer color, as compared to the European Brewery Convention (EBC) colorimetry (Sun et al. 2004).
The content of impurities was determined in virgin olive oil samples, by conducting analysis using RGB imaging technology (Marchal et al. 2013). The quantification of total quantity of bacteria in juice has been carried out using this technique
(Jin and Yin 2010). RGB color imaging was employed to determine the size and
velocity of bubbles in beer (Hepworth et al. 2004). The technology was also used
for the characterization of honey (Shafiee et al. 2013).
Conclusions
RGB color imaging has proved itself to be very reliable and efficient for performing
tasks that are not possible with other methods. Its accuracy and cost-effectiveness
can make the technology feasible to reduce industrial dependence on human graders and can enhance the confidence in consumers in the safety and quality of food
products. The technology also allows evaluating many aspects of a sample, such as
its color, shape, size, and defects. The digital imaging method enables measurement
and analysis of the color of food samples, sufficient to carry out the food engineering research. Mainly because of the low cost, simplicity, and versatility, it might
prove itself as an attractive alternative to other more sophisticated techniques. The
processing speed in large rapidly growing industries may be insufficient, and it may
not be possible to produce accurate results in real time. Therefore, it could lead to a
failure to meet the demands of modern manufacturing requirements. However, the
computing capacity of the computers in the near future will be fast growing and this
could facilitate the handling of large data quickly in real time.
References
Abdullah MZ, Aziz SA, Mohamed AMD (2000) Quality inspection of bakery products using a
color-based machine vision system. J Food Qual 23:39–50
Alirezaei M, Zare D, Nassiri SM (2013) Application of computer vision for determining viscoelastic characteristics of date fruits. J Food Eng 118:326–332
Batchelor MM, Searcy SW (1989) Computer vision determination of stem/root joint on processing carrots. J Agric Eng Res 43:259–269
Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision—
a review. J Food Eng 61:3–16
5 Visible Light Imaging
83
Chen YR, Chao K, Kim MS (2002) Machine vision technology for agricultural applications.
Comput Electron Agric 36:173–191
Chmiel M, Slowinski M, Dasiewicz K (2011) Application of computer vision systems for estimation of fat content in poultry meat. Food Control 22:1424–1427
Costa C, Menesatti P, Aguzzi J, D’Andrea S, Antonucci F, Rimatori V et al (2010) External shape
differences between sympatric populations of commercial clams tapes decussates and T.
Philippinarum. Food Bioprocess Technol 3(1):43–48
Davidson VJ, Ryks J, Chu T (2001) Fuzzy models to predict consumer ratings for biscuits based
on digital features. IEEE Trans Fuzzy Syst 9(1):62–67
Dowlati M, Mohtasebi SS and Guardia MDL (2012) Application of machine vision techniques to
fish-quality assessment Trends anal chem 40:168–179
Du CJ, Sun D-W (2004) Recent developments in the applications of image processing techniques
for food quality evaluation. Trends Food Sci Technol 15:230–249
Du C-J, Sun D-W (2006) Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 72(1):39–55
Du C-J and Sun D-W (2008) Object classification methods Computer vision technology for food
quality evaluation, Elsevier, 81–83
ElMasry G, Sun D-W (2010) Principles of hyperspectral imaging technology. Hyperspectral
imaging for food quality analysis and control. Academic Press, San Diego, California, USA,
pp 3–43
ElMasry G, Cubero S, Moltó E, Blasco J (2012) In-line sorting of irregular potatoes by using
automated computer-based machine vision system. J Food Eng 112:60–68
Fairchild MD (2005) Color appearance models, 2nd edn. Wiley, England
Fernandez-Vazquez R, Stinco CM, Melendez-Martinez AJ, Heredia FJ, Vicario IM (2011) Visual
and instrumental evaluation of orange juice color: a consumers’ preference study. J Sens Stud
26:436–444
Gerrard DE, Gao X, Tan J (1996) Beef marbling and colour score determination by image processing. J Food Sci 61(1):145–148
Girolami A, Napolitano F, Faraone D, Braghieri A (2013) Measurement of meat color using a
computer vision system. Meat Sci 93:111–118
Go¨kmen V, Senyuva HZ, Du¨lek B, Cetin AE (2007) Computer vision-based image analysis for
the estimation of acrylamide concentrations of potato chips and French fries. Food Chem
101:791–798
Gonsalves CMB, Coutinho AP and Marrucho IM (2011) Poly (lactic acid) synthesis, structures,
properties, processing and application, Wiley, 97–100
Gonzalez RC and Woods RE (2007) Digital image processing. (3rd Ed). Pearson International
Edition, 416–429
Gunasekaran S (1996) Computer vision technology for food quality assurance. Trends Food Sci
Technol 7:245–256
Gunasekaran S, Ding K (1993) Using computer vision for food quality evaluation. Food Technol
6:151–154
Hatcher DW, Symons SJ (2000) Influence of sprout damage on oriental noodle appearance by
image analysis. Cereal Chem 77:380–387
Hatcher DW, Symons SJ, Manivannan U (2004) Developments in the use of image analysis for
the assessment of oriental noodle appearance and color. J Food Eng 61:109–117
Hayashi S, Kanuma T, Ganno K and Sakaue O (1998) Cabbage head recognition and size estimation for development of a selective harvester. In ASAE Annual International Meeting, Paper
No 983042, ASAE. St. Joseph, Michigan, USA
Heinemann PH, Hughes R, Morrow CT, Sommer HJ, Beelman RB, Wuest PJ (1994) Grading of
mushrooms using a machine vision system. Trans ASAE 37(5):1671–1677
Hepworth N, Hammond J, Varley J (2004) Novel application of computer vision to determine
bubble size distributions in beer. J Food Eng 61(1):119–124
84
N. Udayakumar
Howarth MS and Searcy SW (1992) Inspection of fresh carrots by machine vision. In: Food Processing
Automation II Proceedings of the ASAE Conference . St. Joseph, Michigan, USA, 1992
Jackman P, Sun D-W, Allen P (2011) Recent advances in the use of computer vision technology
in the quality assessment of fresh meats. Trends Food Sci Technol 22:185–197
Jha SN (2010) Non–destructive evaluation of food quality. Springer, Heidelberg, pp 18–22
Jin S, Yin Y (2010) Research on rapid detection of total bacteria in juice based on biometic pattern recognition and machine vision. In: 2010 3rd IEEE international conference on computer science and information technology (ICCSIT), vol 6, pp 395–399
Kause A, Stien LH, Rungruangsak-Torrissen K, Ritola O, Ruohonen K, Kiessling A (2008)
Image analysis as a tool to facilitate selective breeding of quality traits in rainbow trout.
Livestock Sci 114(2):315–324
Kilic K, Boyacı IH, Koksel H, Ku¨smenog˘lu I (2007) A classification system for beans using
computer vision system and artificial neural networks. J Food Eng 78:897–904
Klibansky N, Juanes F (2008) Procedures for efficiently producing high-quality fecundity data on
a small budget. Fish Res 89(1):84–89
Kondo N, Ahmada U, Montaa M, Muraseb H (2000) Machine vision based quality evaluation of
Iyokan orange fruit using neural networks. Comput Electron Agric 29(1–2):135–147
Lana MM, Tijskens LMM, van Kooten O (2005) Effects of storage temperature and fruit ripening
on firmness of fresh cut tomatoes. Postharvest Biol Technol 35:87–95
Leon K, Mery D, Pedreschi F, Leon J (2006) Color measurement in L*a*b* units from RGB
digital images. Food Res Int 39:1084–1091
Linker R, Cohen O, Naor A (2012) Determination of the number of green apples in RGB images
recorded in orchards. Comput Electron Agric 81:45–57
Liu J and Paulsen MR (1997) Corn whiteness measurement and classification using machine
vision ASAE Annual International Meeting, Technical Papers, Paper No. 973045, 1997
Liu L, Cozzolino D, Cynkar WU, Gishen M, Colby CB (2006) Geographic classification of
Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy
combined with multivariate analysis. J Agric Food Chem 54(18):6754–6759
Liu F, He Y, Wang L, Pan H (2007) Feasibility of the use of visible and near infrared spectroscopy to assess soluble solids content and pH of rice wines. J Food Eng 83:430–435
Liu F, Ye X, He Y and Wang L (2009) Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas. J Food Eng 93:127–133
Loannou I, Perrot N, Hossenlopp J, Mauris G, Trystram G (2002) The fuzzy set theory: a helpful tool for the estimation of sensory properties of crusting sausage appearance by a single
expert. Food Qual Prefer 13(7–8):589–595
Lou W, Nakai S (2001) Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity. Food Res Int
34:573–579
Lu J, Tan J, Shatadal P, Gerrard DE (2000) Evaluation of pork color by using computer vision.
Meat Sci 56:57–60
Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71:100–121
Marchal PC, Gila DM, García JG, Ortega JG (2013) Expert system based on computer vision to
estimate the content of impurities in olive oil samples. J Food Eng 119:220–228
Martin MLGM, Ji W, Luo R, Hutchings J, Heredia FJ (2007) Measuring colour appearance of
red wines. Food Qual Prefer 18:862–871
Matiacevich S, Silva P, Enrione J, Osorio F (2011) Quality assessment of blueberries by computer vision. Procedia Food Sci 1:421–425
McDonald T, Chen YR (1990) Separating connected muscle tissues in images of beef carcass
ribeyes. Trans ASAE 33(6):2059–2065
Mendoza F, Aguilera JM (2004) Application of image analysis for classification of ripening
bananas. J Food Sci 69:E471–E477
Mery D, Chanona-Pérez JJ, Soto A, Miguel Aguilera J, Cipriano A, Veléz-Rivera N, ArzateVázquez I, Gutiérrez-López GF (2010) Quality classification of corn tortillas using computer
vision. J Food Eng 101:357–364
5 Visible Light Imaging
85
Munkevik P, Hall G, Duckett T (2007) A computer vision system for appearance-based descriptive sensory evaluation of meals. J Food Eng 78:246–256
Nagata M, Cao Q, Bato PM, Shrestha BP and Kinoshita O (1997) Basic study on strawberry
sorting system in Japan. Annual International Meeting Technical Papers, Paper No. 973095,
ASAE, 2950 Niles Road, St. Joseph, Michigan 49085-9659, USA
Narendra VG, Hareesh KS (2010) Quality inspection and grading of agricultural and food products by computer vision—a review. Int J Comput Appl 43(2):975–8887
Novell CG, Marin DP, Amigo JM, Novales JF, Guerrero JE, Varo AG (2012) Grading and color
evolution of apples using RGB and hyperspectral imaging vision cameras. J Food Eng
113(2):281–288
Pace B, Cefola M, Renna F, Attolico G (2011) Relationship between visual appearance and
browning as evaluated by image analysis and chemical traits in fresh-cut nectarines.
Postharvest Biol Technol 61:178–183
Park B, Chen YR, Nguyen M, Hwang H (1996) Characterising multispectral images of tumorous,
bruised, skin-torn, and wholesome poultry carcasses. Trans ASAE 39(5):1933–1941
Payne A, Walsh K, Subedi P, Jarvis D (2013) Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging. Comput Electron Agric
100:160–167
Pearson TC, Slaughter DC (1996) Machine vision system for automated detection of stained pistachio nuts. Trans ASAE 39:1203–1207
Rigney MP, Brusewitz GH, Kranzler GA (1992) Asparagus defect inspection with machine
vision. Trans ASAE 35(6):1873–1878
Robertson GL (2012) Food packaging principles and practice, 3rd edition, 326. CRC Press,
Taylor and Francis group, UK
Rodríguez-Pulido FJ, Ferrer-Gallego R, González-Miret ML, Rivas-Gonzalo JC, EscribanoBailón MT, Heredia FJ (2012) Preliminary study to determine the phenolic maturity stage of
grape seeds by computer vision. Anal Chim Acta 732:78–82
Russ JC (1999) Image processing handbook, 3rd edn. CRC Press & IEEE Press, USA
Sapirstein HD (1995) Quality control in commercial baking: machine vision inspection of crumb
grain in bread and cake products. In: Food Processing Automation IV Proceedings of the
FPAC Conference, ASAE. St. Joseph, Michigan, USA
Scanlon MG, Roller R, Mazza G, Pritchard MK (1994) Computerized video image-analysis to
quantify color of potato chips. Am Potato J 71:717–733
Scott A (1994) Automated continuous online inspection, detection and rejection. Food Technol
Europe 1(4):86–88
Shafiee S, Minaei S, Moghaddam-Charkari N, Ghasemi-Varnamkhasti M, Barzegar M (2013)
Potential application of machine vision to honey characterization. Trends Food Sci Technol
30:174–177
Shearer SA, Holmes RG (1990) Plant identification using colour co-occurrence matrices. Trans
ASAE 33(6):2037–2044
Shearer SA, Payne FA (1990) Colour and defect sorting of bell peppers using machine vision.
Trans ASAE 33(6):2045–2050
Sivertsen AH, Chu C-K, Wang L-C, Godtliebsen F, Heia K, Nilsen H (2009) Ridge detection
with application to automatic fish fillet inspection. J Food Eng 90(2):317–324
Stone ML, Kranzler GA (1992) Image based ground velocity measurement. Trans ASAE
35(5):1729–1734
Sun DW (2000) Inspecting pizza topping percentage and distribution by a computer vision
method. J Food Eng 44:245–249
Sun DW, Brosnan T (2003) Pizza quality evaluation using computer vision–part 1 Pizza base and
sauce spread. J Food Eng 57(2003):81–89
Sun FX, Chang YW, Zhou ZM, Yu YF (2004) Determination of beer color using image analysis.
J Am Soc Brew Chem 62:163–167
Sun D-W (ed) (2008) Computer vision technology for food quality evaluation. Academic Press,
Waltham
86
N. Udayakumar
Tan J, Gao X, Gerrard DE (1999) Application of fuzzy sets and neural networks in sensory analysis.
J Sens Stud 14:119–138
Tao Y, Heinemann PH, Varghese Z, Morrow CT, Sommer HJIII (1995) Machine vision for colour
inspection of potatoes and apples. Trans ASAE 38:1555–1561
Tarbell KA, Reid JF (1991) A computer vision system for characterising corn growth and development. Trans ASAE 34(5):2245–2249
Throop JA, Aneshansley DJ, Upchurch BL (1993) Near-IR and color imaging for bruise detection on Golden Delicious apples Proc SPIE 1836, 33–44
Tillett RD (1990) Image analysis for agricultural processes division note DN 1585, Silsoe
Research Institute
Timmermans AJM (1998) Computer vision system for online sorting of pot plants based on
learning techniques. Acta Horticulturae 421:91–98
Tothill I (2003) Rapid and on-line instrumentation for food quality assurance. Woodhead
Publishing, UK, pp 8–13
Ulrici A, Foca G, Lelo MC, Volpelli LA, Fiego DPL (2012) Automated identification and visualization of food defects using RGB imaging: application to the detection of red skin defect of
raw hams. Innovative Food Sci Emerg Technol 16:417–426
Unklesbay K, Unklesbay N, Keller J (1986) Determination of internal color of beef ribeye steaks
using digital image-analysis. Food Microstruct 5:227–231
Wang H-H, Sun D-W (2001) Evaluation of functional properties of cheddar cheese using a computer vision method. J Food Eng 49:49–53
Wang H-H, Sun D-W (2002a) Melting characteristics of cheese: analysis of effect of cheese
dimensions using computer vision techniques. J Food Eng 52:279–284
Wang H-H, Sun D-W (2002b) Melting characteristics of cheese: analysis of effects of cooking
conditions using computer vision technology. J Food Eng 51:305–310
Wang Q, Wang H, Xie L, Zhang Q (2012) Outdoor color rating of sweet cherries using computer
vision. Comput Electron Agric 87:113–120
Wu D, Sun D-W (2013) Colour measurements by computer vision for food quality control—a
review. Trends Food Sci Technol 29:5–20
Yam KL, Papadakis SE (2004) A simple digital imaging method for measuring and analyzing
color of food surfaces. J Food Eng 61:137–142
Yan YL, Zhao LL, Han DH, Yang SM (2005) The foundation and application of near-infrared
spectroscopy analysis, 32, 1st edn. China Light Industry Press, Beijing
Zheng C, Sun D-W, Zheng L (2006a) Recent developments and application of image features for
food quality evaluation and inspection—a review. Trends Food Sci Technol 17:642–655
Zheng C, Sun D-W, Zheng L (2006b) Correlating color to moisture content of large cooked beef
joints by computer vision. J Food Eng 77:858–863
Chapter 6
Near-infrared Imaging and Spectroscopy
V. Chelladurai and D. S. Jayas
Introduction
In the electromagnetic spectrum, near-infrared (NIR) region covers between 780 and
2,500 nm, with the photon energy in the range of 2.65 × 10−19 to 7.96 × 10−20 J
and wavenumbers from 13,300 to 4,000 cm−1. In early 1800s, Fredrick William
Herschel, German-born British astronomer and a music composer, accidently discovered the first invisible region of light from the light spectrum. The application of
NIR spectrum expanded dramatically in last 3 decades, and the development in both,
instrumentation and data analysis techniques of NIR spectroscopy, expanded the
application range to chemical analysis, agricultural and food product analysis, and
more. The developments of new NIR techniques such as NIR imaging (NIR cameras,
NIR hyperspectral imaging systems), Fourier transform (FT)-NIR spectroscopy,
NIR microscopes, and NIR thermal cameras extend the application of near-infrared
band, because some of these techniques give spectral as well as spatial data which
help to analyse chemical constituents as well as physical and textural parameters of
a sample. But, the use of NIR measurement was very limited in the early days, and
first qualitative measurement was done in 1912 at the Mount Wilson observatory
by F. E. Fowle, who determined the atmospheric moisture (Kaye 1954). Amount
of water in gelatin was measured using NIR in 1938 by Ellis and Bath (1938). The
growing demand for a rapid method for determination of protein, moisture, and
oil content of agricultural produces in 1950s drove researchers towards the use of
NIR spectroscopy. Kari Norris, who was working in USDA, first tested the application
of NIR spectroscopy to determine the moisture, protein, and oil content of agricultural
­products (Hindle 2008). Moisture in the soybean seed was determined by the methanol
V. Chelladurai · D. S. Jayas (*)
Department of Biosystems Engineering, University of Manitoba, Winnipeg,
MB R3T 2N2, Canada
e-mail: Digvir.Jayas@umanitoba.ca
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_6, © Springer-Verlag Berlin Heidelberg 2014
87
88
Fig. 6.1 Schematic diagram
of NIR imaging system
V. Chelladurai and D. S. Jayas
Light source
Sample
Optical lens
Filter
Detector
extract of the seeds using NIR spectrum in 1962 by Norris and his colleagues (Hart
et al. 1962), and they followed with the determination of moisture in intact seeds using
transmittance spectroscopy with carbon tetrachloride (CCl4) which was used to reduce
the scattering losses (Norris and Hart 1965). Bern-Gera and Norris (1968a) then published their work on application of multiple linear regression (MLR) to the calibration
of problems related to agricultural products. Nowadays, NIR imaging and spectroscopy is one of the preferred quality monitoring methods in the food industry. Nearinfrared techniques are used for qualitative analysis of agricultural products (grains,
oilseeds, fruits, and vegetables), feed and forage, dairy products, meat, and meat products. These methods are also used to determine food adulterations.
Near-infrared Imaging
Theory
Near-infrared imaging is similar to optical digital imaging, but the detector in the
NIR imaging system captures the image only in the near-infrared region. When an
object is illuminated with light, it absorbs, reflects, and transmits light at various
composition based on its physical and chemical properties. In near-infrared imaging systems, this absorbed, transmitted, or reflected radiation only at NIR waveband is captured using a NIR detector or sensor. The filter in the NIR imaging
systems helps to capture the image only at NIR waveband (Fig. 6.1). The signals
detected by the NIR detector then are processed by the electronic modules in the
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camera and stored in a computer using special software provided along with the
camera by the manufacturers.
Instruments
NIR Imaging Camera
Components of NIR Imaging System
The main components of NIR imaging systems are an optical lens, filter, detector,
connector, computer, and software. The optical lens concentrates the light flow on the
detector surface and helps to form the image of the object. In most of the NIR imaging
systems, the lens corrects over the entire NIR spectrum (900–1,700 nm). Selection of
useful spectral band plays a major role in deciding the applications of the NIR imaging system and to maximize the amount of useful information from the system. The
filters do the job of spectral selectivity and these filters allow passing of only a specific
part of NIR waveband or entire NIR waveband based on the application of that imaging system. The detector or the sensor is the main part of the NIR imaging system,
which detects and measures the NIR radiation reflected or transmitted by the object.
Most of the NIR imaging systems use indium gallium arsenide (InGaAs) detector or
mercury cadmium telluride (MCT) detector. The InGaAs detectors have very high sensitivity in the NIR range (900–1,700 nm), and the MCT detectors are sensitive in the
range of 800–2,500 nm. A modified version of InGaAs detector, known as VisGaAs
detector also used in some advanced imaging systems, has the spectral sensitivity of
400–1,700 nm (visible and NIR region of the spectrum). Nowadays, most of the detectors consist of focal plane array (FPA) of 320 × 256 pixels with a 25–30-µm pitch.
The NIR imaging systems are connected with the computer through different types
of connectors: GigaBit-Ethernet connectors, frame grabber, and trigger in/out connections. Some NIR imaging systems are equipped with the electronic modules which
process the signal from the NIR detector and apply corrections like on-camera nonuniformity corrections. The computer and the software are used to store and analyse
the images captured by the NIR imaging systems.
Hyperspectral Imaging System
Theory
Spectroscopy technique provides spectral data of an object over near-infrared
spectral region, but it does not provide any spatial data. The regular imaging technique provides only spatial data but no spectral information; hence, detection
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Fig. 6.2 Tunable filter-type
NIR hyperspectral imaging
system
of chemical components of an object is not possible (Ariana and Lu 2008).
Hyperspectral imaging is a new NIR imaging technique, in which the object is
imaged over a large number of spectral bands and yields complete reflectance
spectrum with spatial (imaging) data. Hyperspectral imaging provides a large data
set, which is called a hypercube, which facilitates a complete analysis of intrinsic properties and external characteristics of samples. Thus, this technique permits
spectroscopic image analysis of a sample using image processing techniques and
chemical sensing methods (Headwall 2012).
Based on sample presentation technique, NIR hyperspectral imaging systems
are classified into 3 groups:
1. Tunable filter system: This is also known as wavelength scanning, in which the
imaging system and sample is fixed, and images of the whole object/sample are
obtained one wavelength after another (Fig. 6.2).
2. Whiskbroom system: This is also known as point scanning, and it uses spectral
scanning concept. In this type of system, complete spectral information of a single
point is collected and then the system collects the spectral information of the next
point.
3. Pushbroom system: This is also known as line-by-line scanning, and this
type also uses spectral scanning concept (Fig. 6.3). In this type of system,
complete spectrum of each point of the object is collected on one spatial line
after another. To image the whole sample, either the sample or the camera
must move. Successive line scans are combined to form a three-dimensional
hypercube.
Area scan hyperspectral imaging system is used mainly to acquire the images of
stationary objects. Both line scan and area scan imaging systems are well suited
for quality inspection of food materials (Kim et al. 2001).
Based on the number of wavelength bands used to acquire the images of
an object, we can divide these systems into multispectral, hyperspectral, and
ultraspectral systems (ElMasry et al. 2012). In multispectral imaging systems,
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Fig. 6.3 Schematic of
pushbroom-type NIR
hyperspectral imaging
system (Reproduced from
Kamruzzaman et al. 2011
with permission from
Elsevier Ltd.)
object is imaged at few selected spectral bands, and the systems have a spectral
resolution of the order of 10. These spectral bands are irregularly placed based
on the intended use of the multispectral imaging system, so these do not produce
a complete “spectrum” of an object. But in hyperspectral imaging systems, the
object is imaged at hundreds of spectral bands with the spectral resolution in the
order of 100, which produce a continuous spectrum (or “spectra”) of all pixels
of the field of interest. The ultraspectral system is commonly used to get spectral
imaging of an object with a very fine spectral resolution (ElMasry et al. 2012).
Components of Hyperspectral Imaging System
The major components of a hyperspectral imaging system, similar to other NIR
spectroscopic instruments, are as follows: radiation or light source, wavelength
selection device (filter), and detector. The unique requirements for NIR hyperspectral imaging systems are the image acquisition software and an integrated computer for data acquisition and storage.
Radiation or Light Source
The illumination source should be able to produce the light sensitive to the camera in the desired wavelength application range. Most of the NIR instruments are
using light-emitting diodes (LED), tungsten halogen lamps, quartz halogen lamps,
and tunable lasers as their sources for producing NIR radiation. The LED lamps
produce light only in the range of 400–900 nm, but tungsten halogen lamps have
the ability of producing light at wide spectral range (400–2,500 nm). Therefore,
tungsten halogen lamps are the most common illumination source in NIR hyperspectral imaging systems and are preferred over other types of light sources
because of high durability and stability (Manley et al. 2008).
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Wavelength Filters
Wavelength filtering devices allow only the desired wavebands of radiation into
the system and remove the out-of-band radiation. The most common types of
waveband filtering devices are optical interference filters, grating devices (e.g.
prism-grating-prism), and electronically tunable filters (ETFs); and the type of
filters used mainly depend on the type of hyperspectral imaging system. Grating
devices are commonly used in pushbroom-type hyperspectral imaging systems, in
which either camera moves (in airborne systems) or the sample moves on belt conveyor. The ETFs are most suitable wavelength filtering devices for area scan imaging (stationary objects) systems. Acousto-optical tunable filter (AOTF) and liquid
crystal tunable filter (LCTF) are two advanced ETFs. The AOTFs and LCTFs have
large optical aperture, high spectral resolution, wide spectral range, and they can
randomly access tuning wavelengths (Wang and Paliwal 2007). These filters also
do not have any moving parts which overcomes the registration problem (distortion in image) in acquired images, a major issue with interference filters and grating devices.
The AOTFs are based on diffraction in which wavelengths are selected by
applying radio frequency (RF) acoustic waves to a crystal material (quartz, TeO2,
and Tl3AsSe3), and wavelength of light produced is proportional to the RF frequency applied. The AOTFs can produce bandwidths as narrow as 1 nm full width
at half maximum (FWHM), but, the field of view (FOV) through AOTFs is smaller
than LCTFs (Call and Lodder 2002).
The LCTFs are built using a stack of polarizers and tunable retardation (birefringent) liquid crystal plates (Tran 2003). The liquid crystal is placed between
two polarizers whose axes are parallel to each other. The unpolarized light from
the light source is converted into linearly polarized light by input polarizer and is
passed through the birefringent crystal. This polarized light splits into ordinary and
extraordinary beams, and a phase delay between the beams is introduced by the
birefringent retarder (quartz or calcite). The optical path difference between these
two beams is called “retardance”, and the light coming out of the retarder passes
through second polarizer which blocks out-of-band transmission. Most of the area
scan hyperspectral imaging systems use LCTF-type filter for wavelength selection.
Detectors
Detectors record the spectra of the sample by reflectance or transmittance mode,
and the detectors in the hyperspectral systems also have the capacity of recording spatial data using suitable image integration software and hardware. The point
scan systems use linear array of lead sulphide (PbS) detectors (1,100–2,500 nm),
silicon detectors (360–1,050 nm), and indium gallium-arsenide (InGaAs) detectors
(900–1,700 nm). In area scan hyperspectral imaging, FPA-type detectors are commonly used to reduce scanning time, to obtain higher signal-to-noise ratios, and
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to overcome image distortion problems (Jayas et al. 2010). Commercially, there
are different types of FPAs available: InGaAs, indium antimonide (InSb), platinum
silicide (PtSi), germanium (Ge), quantum-well infrared photodetectors (QWIPs),
and mercury cadmium telluride (HgCdTe). The InGaAs, InSb, HgCdTe, and
QWIP are the most commonly used detectors in line scan and area scan hyperspectral imaging systems (Tran 2003).
The InGaAs detectors are most commonly used in the 900–1,700 nm wavelength band and have a large range of applicability in agricultural and food
material inspection. They have higher band-gap energy compared to the InSb
detectors, produce very low dark current and can be operated at room temperature by thermoelectric cooling. Indium antimonide (InSb) detectors have wide
spectral response from 1,000 to 5,000 nm. But these InSb FPAs are very expensive, because they require cryogenic cooling to operate under room temperature.
HgCdTe detectors also have high sensitivity, and detectors can be designed to
operate in very wide range of the IR region (2,000–26,000 nm). The biggest drawback of HgCdTe detectors is the instability and non-uniformity of pixels caused by
high Hg vapour pressure during the material growth and thermal expansion mismatch (Tran 2003). Spectral and spatial data from the detector transfer to the computer through standard communication interfaces, e.g. FireWire, Camera Link, and
GigE VISION.
Calibration and Preprocessing
Proper calibration and preprocessing of the collected data are necessary to obtain
useful information from the hyperspectral data. Dark current offset, gain corrections, and variable integration time are the basic image corrections in the hyperspectral imaging. Smoothing, normalization, multiple scatter correction, standard
normal variate, and de-trending are the common preprocessing techniques for
spectral data (Manley et al. 2008).
Analysis of Hyperspectral Data
Once the hyperspectral data are reduced dimensionally, quantitative and qualitative analysis can be performed using either supervised or unsupervised techniques.
Soft independent modelling of class analogy (SIMCA), discriminant partial least
square (DPLS), linear and quadratic discriminant analysis (LDA and QDA), multiple discriminant analysis (MDA), canonical variate analysis (CVA), artificial
neural network (ANN), and k-nearest neighbour are the most commonly used
supervised methods. Principle component analysis (PCA) and hierarchical cluster analysis (HCA) are the most commonly used unsupervised methods for NIR
hyperspectral data analysis (Manley et al. 2008).
V. Chelladurai and D. S. Jayas
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NIR Spectroscopy
Theory
The NIR spectroscopy is the most common technique used for analytical testing
in food and agricultural industry. Initially NIR spectroscopy was mostly used for
grain quality analysis, but now it has a wide range of applications in the food and
agricultural industry. Near-infrared spectrophotometers record the absorption of
NIR radiation by a material. The basic principle of the NIR spectroscopic technique is that, when a material is illuminated by electromagnetic radiation in the
NIR region, the molecules of the materials absorb the light in the NIR region and
vibrate at unique frequencies based on the chemical composition of the material
(Murray and Williams 1987). Chemically simple molecular groupings with strong
interatomic bonds (i.e. carbon–hydrogen, nitrogen–hydrogen, oxygen–nitrogen)
generate NIR spectra (Manley et al. 2008). Most of the food materials contain
these common molecules, and if a food material is illuminated by a light source,
then it will absorb radiation at particular frequencies through the bonds formed
by atoms of the material. The unknown chemical components of the materials can
be determined by detecting this absorbed radiation. The NIR spectrum shape is
characterized by overtones and combination bands of fundamental vibrations
occurring in the NIR region. Due to complex molecular structures of most organic
compounds, the NIR spectra arising from overtones and combination bands have
broad and highly overlapping peaks and valleys (Miller 2001). This makes spectral analysis and extraction of chemical and physical information from the spectra
very difficult. Various statistical and mathematical methods are used to extract and
interpolate the spectral data based on composition of the materials.
If a sample is illuminated by a light source, it will absorb some energy and
transmit or reflect the remaining light energy. The spectroscopic techniques can be
divided into two groups: transmittance spectroscopy and reflection spectroscopy.
Transmission spectroscopy is the most commonly used form of spectroscopy. The
basic principle of transmission spectroscopy is that light passes through a sample
and energy is absorbed by the chemical components of the sample (Fig. 6.4). The
detector measures the amount of light passing through the sample and by comparing with initial intensity of the light; the amount of light absorbed by the sample
can be indirectly measured.
Transmittance, T = I/I0
(6.1)
where,
I is the light energy transmitted through the sample and
I0 is the initial light intensity reaching the sample.
The absorbance (A) of the material can be calculated using the equation;
Absorbance, A = log (1/T )
(6.2)
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Fig. 6.4 Schematic diagram
of transmittance spectroscopy
Light source
Sample
Detector
Fig. 6.5 Schematic diagram
of reflectance spectroscopy
Detector
Light source
Light source
Sample
The Lambert-Beer law explains the relationship between the absorbance and
­concentration of a material:
A = εlc
(6.3)
where,
ε the extinction coefficient of the substance, unique for each substance, M−1 cm−1
l the sample path length, cm
c the molar concentration of the solution.
In reflection spectroscopy, the sample is illuminated by a NIR light source, and
detectors measure the amount of light energy reflected by the samples (Fig. 6.5).
Reflectance spectroscopy uses wavelengths between 1,000 and 2,600 nm
V. Chelladurai and D. S. Jayas
96
(Hruschka 1987). The absorbance of the material can be indirectly calculated from
the reflectance measured by the detectors:
Absorbance, A = log (1/R)
(6.4)
where R is the reflectance.
Instruments
Spectrophotometer
Components NIR Spectroscopy
The major component of NIR spectroscopy instruments are as follows:
1.
2.
3.
4.
5.
Light source
Lenses and mirrors
Wavelength selectors or filters
Monochromators
Detectors.
Light Source
The basic requirement for a NIR spectroscopy light source is that it should emit a
continuous radiation in the range of 900–2,500 nm with light intensity high enough
so that no other signal conditioning (like amplification) is needed. Tungsten–halogen
lamp is commonly used as light source for NIR spectroscopy due to its longer life
and more stability because of halogen’s cleaning action (Workman and Burns 2001).
Lenses and Mirrors
Glass lenses and mirrors are used for visible region. Most of the times, lenses and
mirrors of the infrared instruments are made of special materials, because glass
is opaque to radiation of wavelengths longer than 2,000 nm. Fused quartz is well
suited for NIR applications, and Pyrex is an economical material, but there may be
a chance of up to 10 % diminishing transmission at 2,800 nm. Aluminium and silver
first-surface mirrors are commonly used in NIR instruments (Manley et al. 2008).
Filters
Filters are used to define the wavelength range of NIR instruments. These filters
allow radiation from the light source to monochromater only in particular wavelength range (normally NIR range). A filtering device should have the following
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characteristics: minimal tunability time, minimal out-of-band transmission, minimal
physical thickness, low power consumption, insensitive to polarization, selectable bandpass, insensitive to environment (e.g. ambient temperature and day light
fluctuations), insensitive to angle of incidence of the incoming light (wide field of
view), infinite spectral range, large aperture, constant bandpass, and random access
to wavelengths (Gat 2000). At the beginning, NIR spectrophotometers used tilting
filter concept, in which the incident angle of light passing through the interference
filter wedge defines the transmitted energy. Spinning filter concept uses the same
basic principle, but the filters are mounted in an encoder wheel which provides
higher positioning accuracy and reliability. Introduction of AOTFs in 1990s helped
to enhance the generation of monochromatic energy for NIR instruments. The AOTF
has no moving parts and tellurium dioxide (TeO2) birefringent crystals are commonly used in AOTF filters. More technical details of the AOTFs and other filters are
elaborately discussed in components of NIR hyperspectral imaging system section.
Monochromator
The monochromator disperses or spreads out the radiation according to the
­wavelength. The common types of dispersing elements are prisms and gratings.
The ruled plane and the concave holographic are the two types of gratings. The
ruled plane gratings are made up of glass and have triangle shape parallel grooves
created by a ruling engine with diamond shape tool (McClure 2003). Inaccuracies
in the ruling machine results “ghost” and “grass” errors in the ruled plane gratings. The concave holographic gratings eliminate these kinds of imperfections in
these gratings because two intersecting laser beams produce interference fringes in
a photosensitive material, which creates triangular-shaped grooves. The major disadvantage of gratings is that the light at different wavelengths leaves the gratings
at the same angle of dispersion, which is called as “overlapping orders”. Prisms
do not have this overlapping order, and most of the times the monochromators
with grating system have filters or prisms to eliminate the overlapping order effect
(McClure 2003).
Detectors
The radiation from the light source spreads out or disperses by the monochromator
directed to a sample which absorbs some radiation and reflects or transmits rest
of the radiation. The detector measures this reflected or transmitted radiation. The
spectral response, speed of response, and the minimum radiation power detection
level are the major parameters used to characterize the infrared detectors. Based
on their operation principle, detectors are classified into two classes: thermal
detectors and photon detectors (McClure 2003). The thermal detectors measure
the amount of absorbed thermal energy by a temperature-sensitive material, and
photon detectors measure the response created by photons of the radiation. Lead
sulphide (PbS) detectors are commonly used for measurement in 1,100–2,500
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V. Chelladurai and D. S. Jayas
Fig. 6.6 Vis-NIR
spectrophotometer
range, usually these PbS detectors are sandwiched with silicon photodiodes to
measure in the visible and near-infrared range (400–2,500 nm). Figure 6.6 shows
a commercial Vis-NIR spectrophotometer. Complete discussion about different
types of detectors is given in NIR hyperspectral imaging instrumentation section.
Fourier Transform Near-infrared (FT-NIR) Spectroscopy
Theory
Fourier transform near-infrared spectroscopy is also a technique which collects the
spectrum (absorption, reflectance) of a sample in a wide range of spectra. The name
implies that, Fourier transform method is needed to convert the raw data into original spectrum. The FT-NIR obtains spectra of a material at thousands of data points
with the use of an interferometer, which modulates the NIR signal and the data collection unit (normally a computer). The major advantage of the FT-NIR spectroscopy is the high signal-to-noise ratio and scan speed (McCathy and Kemeny 2008).
These instruments have a light source which emits NIR radiation towards the interferometer. The interferometer consists of a beam splitter and two mirrors, out of
which one is stationary and another is moving. The simple form of interferometer
is Michelson interferometer, which consists of two mirrors placed mutually perpendicular to each other, and a beam splitter (Fig. 6.7). The moving mirror moves
along its axis at a constant velocity. When the NIR radiation is sent to interferometer, beam splitter partially reflects half of the radiation to one mirror and transmits
another half of the radiation to the other mirror. The beams reflected back from both
mirrors are recombined at the beam splitter and directed to the sample. The pathway of the beams to and from the movable mirror is the function of mirror position.
The different positions of mirror create difference in path length of a beam, which
produces interference. The data collected during the motion of the moving mirror,
information in the time domain, contains the spectral information of the sample,
which is retrieved by Fourier transformation (McCathy and Kemeny 2008).
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Fig. 6.7 Schematic view of
Michelson interferometer
Components of FT-NIR Spectroscopy
Light Source
The light source supplies light in the NIR range. Commonly, halogen bulbs with
wattage of 5–50 W are used because of throughput advantage and long life.
Interferometer
The NIR energy from the light source is directed to interferometer, which consists
of two mirrors and beam splitter. The light energy from the light source splits into
two halves, one half reflected to fixed mirror and another half to moving mirror
by the beam splitter. Then, the beams reflected from fixed and moving mirrors are
recombined at the beam splitter and directed out to the sample. The beam splitters
are made up of quartz or CaF2 or KBr substrate with varying proprietary layer
coatings. The interference between the beams depends on the optical path of the
beams or retardation. If the fixed and moving mirrors are at equal distance from
the beam splitter, then the retardation is zero, which means all the energy from
the source reaches the detector. The variation in intensity, when the moving mirror is at different positions, contains the spectral information of the sample, which
is retrieved by the application of Fourier transformation. The mirrors are flat and
front surfaced with gold or aluminium.
Typically, FT-NIR spectrophotometers use helium–neon (HeNe) laser to control
the moving mirror and ensure alignment of interferometer. The intensity of the signal detected by the detector I(t) is:
I(t) = Γ cos(4πvαt)
(6.5)
where α is the optical frequency of the HeNe (15,802.78 cm−1), v is the mirror
velocity (cm s−1), t is the time (s) (McCarthy and Kemeny 2008).
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V. Chelladurai and D. S. Jayas
Detector
The sample absorbs some energy directed to it by the beam splitter and transmits or
reflects the remaining energy based on its chemical property to the detector. Similar
to NIR spectroscopy, and NIR hyperspectral imaging systems, PbSe, PbS, InSb, or
InGaAs detectors are used in FT-NIR spectroscopy. Normally, detectors with fast
response are used in FT-NIR spectroscopy because of the need for high scanning speed.
Applications in Agriculture and Food Industry
Near-infrared spectroscopy techniques are becoming popular tool for quality analysis
of cereal grains, dairy products, meat, and meat products. Producers’ and processing
industries’ preference for non-destructive quality analysis and quality control methods is the reason behind the increase in use of NIR spectroscopic methods in the agricultural and food industry in last decade, but the application of NIR in food industry
started in 1,938 itself, when Ellis and Bath (1938) tested the NIR absorption spectrum of water in gelatin. Ben-Gera and Norris (1968a, b) demonstrated the application of NIR spectroscopy to determine fat and moisture in meat, moisture content of
soybeans, and fat content in milk. They used the wavelengths of 1,680, 1,940, 2,100,
2,180, 2,230, and 2,310 nm for the above applications. Later, Norris et al. (1976)
also tested the application of NIR spectroscopy to analyse the nutritive value of feed
materials. They recommended the wavelengths of 1,672, 1,700, 1,940, 2,100, 2,180,
and 2,336 nm for quality analysis of forage materials. Now, most of the grain handling facilities and food processing industries use NIR spectroscopic techniques for
wide range of applications such as quantification of chemical composition of grains
(Delwiche 1995, 1998; Guy et al. 1996; Miralbés 2004; Osborne et al. 1993; Wang
et al. 2004b; Wesley et al. 2001); detection of food adulteration (Cocchi et al. 2006);
the detection of insect and fungal damages in grain (Baker et al. 1999; Delwiche
2003; Singh et al. 2009a; Maghirang et al. 2003; Perez-Mendoza et al. 2003; Wang
et al. 2004a); the detection of defects in fruits, vegetables, and grains (Ariana et al.
2006; Dowell 2000; Lu 2003; Mehl et al. 2004; Xing et al. 2005; Wang et al. 2001);
and also the detection of toxins in grains and food materials (Pearson et al. 2001;
Pettersson and Åberg 2003; Ruan et al. 2002).
Applications of NIR Imaging
Cereal Grains
Application of NIR imaging techniques in cereal grains range from class identification, foreign material detection to detection of mycotoxins. Wheat classes were identified using NIR hyperspectral imaging systems based on their chemical compositions
(Mahesh et al. 2008; Williams et al. 2009). Choudhary et al. (2009) extracted wavelet
6
Near-infrared Imaging and Spectroscopy
101
Fig. 6.8 a Short-wave infrared (SWIR) hyperspectral imaging system; b hyperspectral images
of healthy and insect-infected wheat kernels (Reproduced from Singh et al. 2009a with permission from Elsevier Science)
features of NIR hyperspectral imaging data and identified the wheat classes in the
wavelength region of 960–1,700 nm. Maize was classified based on hardness levels
using NIR hyperspectral imaging system (Williams et al. 2009). Vitreousness of hard
wheat is the glossy or shiny appearance of the wheat kernel, and it is an indicator of
high hardness and high protein content. Vitreousness affects the milling performance,
and Vis-NIR and SWIR-NIR hyperspectral imaging systems were used to identify
vitreousness (Gorretta et al. 2006; Shahin and Symonds 2008). Singh et al. (2009b)
detected midge-damaged wheat kernels using images acquired using a short-wave NIR
hyperspectral imaging system (700–1,100 nm) and an area scan digital colour camera
and got 95.3–99.3 % classification accuracy using the combined NIR hyperspectral and
top 10 colour image features. Knowledge of chemical composition of cereal grains and
moisture content plays a major role in grain grading and processing operations. Nearinfrared hyperspectral transmittance imaging in the wavelength region of 750–1,090 nm
was evaluated for predicting the constituent concentrations and analysing the quality of
single kernels of maize (Cogdill et al. 2004). Insect infestation in the cereal grains in
field and during storage downgrades the quality of grain and lowers the market value.
Internal feeding insects cause internal damages and are difficult to identify visually. NIR
cameras and hyperspectral cameras have been used to detect the internal infestation in
cereal grains (Fig. 6.8) by the insects (Singh et al. 2009a; Ridgway and Chambers 1998).
Fruits and Vegetables
An automatic system for bruise and other defects detection in fruits and vegetables will help the farmers to reduce potential economic losses and increase the
net profit and will also help consumers to get better-quality products. The NIR
102
V. Chelladurai and D. S. Jayas
Fig. 6.9 NIR hyperspectral imaging system for detecting bruises of pickling cucumbers
(Reproduced from Ariana et al. 2006 with permission from Elsevier B.V.)
hyperspectral imaging techniques have been tested for bruise detection in fruits
and vegetables (Ariana et al. 2006; Lu 2003; Xing et al. 2005), detection of fecal
contamination (Kim et al. 2002; Lefcout et al. 2006) and surface defects in fruits
(Mehl et al. 2004), and measurement of bitter pit in apples (Nicolaï et al. 2006).
Most of the studies found that wavelength range of 1,000–1,340 nm provides more
details for detection of defects in fruits and vegetables. Quality attributes of fruits
and vegetables (moisture content, total soluble solids content, and acidity) can be
analysed using NIR imaging techniques (ElMasry et al. 2007) and inclusion of
visible wavelength along with NIR waveband (350–1,700 nm) provided more useful data for assessing quality parameters of fruits (apple, citrus, peach, strawberry,
and cherry) and vegetables (potato, and cucumber) (Fig. 6.9) (Gowen et al. 2007).
Meat and Meat Products
Meat consumption is increasing day by day in developing countries and most
of the consumers are paying more attention to meat quality. Both pre-slaughter
(breed of animal, weight, and growing environment) and post-mortem (storage
temperature and time) have major influence of the quality attributes of meat (Venel
et al. 2001). Growing consumer market, increased awareness about meat quality among the consumers and strict food safety regulations drive meat processing
industries to adopt reliable and rapid quality analysis methods like NIR imaging
systems. The NIR technique is less time consuming (50–60 s), non-destructive and
requires minimum or no sample preparation, and it is possible to analyse multiple quality attributes (fat, protein, tenderness and moisture) in a single test. Other
advantages are that NIR techniques are chemical-free analysis tools and cost
per measurement is low. Near-infrared hyperspectral systems have the ability to
predict tenderness of meat (Naganathan et al. 2008a, b). Adulteration is a major
6
Near-infrared Imaging and Spectroscopy
103
Fig. 6.10 a NIR hyperspectral imaging system for beef tenderness prediction; b hyperspectral
images of beef at different wavelengths (Reproduced from Naganathan et al. 2008b with permission
from Elsevier B.V.)
104
V. Chelladurai and D. S. Jayas
Fig. 6.11 a NIR hyperspectral imaging system for pork quality assessment; b hyperspectral
images of pork at different wavelengths (Reproduced from Qiao et al. 2007 with permission from
Elsevier B.V.)
concern in meat industry, and NIR imaging techniques have the capability to predict minced lamb meat adulteration (Kamruzzaman et al. 2012). Hildrum et al.
(2004) used a NIR instrument with diode array detector for measuring fat, water
and protein content of ground beef, and the prediction models developed from the
reflectance data had correlation coefficients between 0.93 and 0.96. NIR imaging systems have been used to detect the microbial contaminations in the meat
(Lawrence et al. 2003; Peng and Wu 2008; Peng et al. 2009) and also to conduct
for online inspection of poultry products (Yang et al. 2009). NIR hyperspectral
imaging systems used for measuring beef and pork quality assessments are shown
in Figs. 6.10, 6.11, and 6.12 shows the schematic view of the NIR imaging system
used for fat and moisture measurement of fish fillets.
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Near-infrared Imaging and Spectroscopy
105
Fig. 6.12 NIR spectral imaging system for fish fillets fat and moisture prediction (Reproduced
from ElMasry and Wold 2008 with permission from American Chemical Society)
A summary of applications of NIR imaging techniques in agricultural and food
industry is given in Table 6.1.
Applications of NIR Spectroscopy
Cereal Grains
Applications of NIR spectroscopy for quality determination of cereal grains were
started as early as 1938 and now most of the grain quality analysis processes are
done by NIR spectroscopy. Wheat is the first cereal crop analysed using NIR spectroscopy. Wheat is classified into several classes based primarily on colour, hardness, and growing season for fixing grain trading price and also to separate for
various end use applications. Wheat was classified based on hardness, milling,
and baking quality parameters (Bertrand et al. 1985; Delwiche and Massie 1996;
Delwiche and Norris 1993; Delwiche et al. 1995; Downey 1986; Dowell 1997;
Mohan et al. 2005; Slaughter et al. 1992). Some of the researchers used visible
range along with NIR to get higher classification accuracy. Maghirang and Dowell
(2003) measured hardness of bulk wheat in the range of 400–1,700 nm and found
that more than 97 % kernels were correctly classified as soft or hard wheat with
550–1,690 nm wavelength range. The NIR analysis successfully identified vitreous wheat kernels (Dowell 2000; Wang et al. 2002). Waxy and wild wheat varieties were also identified using NIR spectroscopy in the range of 1,100–2,498 nm
(Delwiche and Graybosch 2002). Detection of protein and moisture content of
cereal grains is required for grain grading process. Scientific studies proved the
NIR hyperspectral
imaging
Absorbance
NIR imaging
Wheat
Wheat
Wheat
Reflectance
Reflectance
Reflectance
Wheat
Dates
Apple
Wheat
Cereal grains
Fruits and
vegetables
Wheat
Cereal grains
Wheat
Product
Product type
Reflectance
Reflectance
Reflectance
(Vis-NIR)
Reflectance
Transmittance
Mode
Table 6.1 Application of NIR imaging techniques in agricultural and food industry
Technique
Classification of
wheat classes
Identification of
wheat classes
and moisture
level
Detection of insect
damage
Detection of midge
damage
Identification of
vitreousness
DON and Fusarium
infection
Detection of
Fusarium
infection
Detection of faecal
contamination
Prediction of total
soluble solid
Analysis
Singh et al. (2009a)
750–1,700
1,000–1,600
650–1,100
900–2,500
(continued)
Gorretta et al. (2006)
Shahin and Symons
(2008)
Singh et al. (2009b)
Mahesh et al. (2011)
960–1,700
700–1,100
Manickavasagan and
Ganeshmoorthy
(2013)
Mahesh et al. (2008)
900–1,700
Kim et al. (2002)
Polder et al. (2005)
900–1,750
450–850
Peris et al. (2009)
Reference(s)
950–1,650
Wavelength range
(nm)
106
V. Chelladurai and D. S. Jayas
Apple
Apple
Apple
Apple
Reflectance
Reflectance
Apple
Apple
Apple
Reflectance
Reflectance
Reflectance
Reflectance
Maize
Reflectance
Wheat
Wheat
Wheat
Product
Maize
Fruits and
vegetables
Product type
Transmittance
Reflectance
Table 6.1 (continued)
Technique
Mode
Classification using
wavelet features
Detection of fungal
infection
Prediction of αamylase content
Prediction of moisture,
protein and oil
content
Class identification
based on hardness
Detection of bruises
Detection of bruises
Detection of faecal
contaminations
Detection of surface
defects and
contaminations
Measurement of
bitter pits
Defects detection
Detection of faecal
contaminants
Analysis
Nicolaï et al. (2006)
900–1,700
(continued)
Lee et al. (2005)
Liu et al. (2007)
Mehl et al. (2004)
430–900
418–918
447–951
Xing et al. (2005)
Lu (2003)
Kim et al. (2002)
400–1,000
900–1,700
450–851
Williams et al. (2008)
Cogdill et al. (2004)
750–1,090
1,000–2,498
Xing et al. (2009)
Choudhary et al.
(2009)
Zhang et al. (2007)
Reference(s)
1,000–2,500
1,000–1,600
Wavelength range
(nm)
1,000–1,600
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Near-infrared Imaging and Spectroscopy
107
Measurement of
sugar content
and firmness
Starch index
determination
Measurement of
fruit skin and
flesh colour,
firmness, soluble
solids content,
and titratable
acid
Measurement of
firmness and
soluble solids
content
Determination of
moisture, total
soluble solids
(TSS), and pH
Prediction of firmness
and soluble solids
content
Measurement of
firmness
Freeze damage
detection
Apple
Strawberry
Strawberry
Strawberry
Peach
White button
mushroom
Reflectance
Reflectance
Reflectance
Scattering
Reflectance
Apple
Analysis
Product
Apple
Product type
Transmittance
Table 6.1 (continued)
Technique
Mode
Nagata et al. (2005)
ElMasry et al. (2007)
Nagata et al. (2004)
Lu and Peng (2006)
650–1,000
400–1,000
450–650
500–1,000
(continued)
Gowen et al. (2009)
Noh and Lu (2007)
500–1,040
400–1,000
Peirs et al. (2003)
Ariana and Lu (2002)
Reference(s)
900–1,700
Wavelength range
(nm)
900–1,500
108
V. Chelladurai and D. S. Jayas
Pork
Pork
Poultry
Poultry
Poultry
Poultry
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance/
Transmittance
Beef
Cucumber
Cucumber
Product
Beef
Meat and meat
products
Product type
Reflectance
Reflectance
Reflectance
Reflectance
Table 6.1 (continued)
Technique
Mode
Microbial spoilage
prediction
Classification and
estimation of
marbling
Detecting viable
count of bacteria
Surface contaminant
detection
Detection of
contaminants on
poultry carcasses
Online inspection
of slaughtered
chicken
Detection of bone
fragments in
chicken breast
Detection of bruises
Inspection of chilling
damages
Prediction of
tenderness
Analysis
Nakariyakul and
Casasent (2008)
Yang et al. (2009)
Yoon et al.
(2006; 2008)
400–1,024
400–1,000
400–1,000
(continued)
Lawrence et al. (2004)
400–900
400–1,100
Peng and Wu (2008)
Qiao et al. (2007)
430–1,000
900–1,700
400–1,100
400–1,100
Ariana et al. (2006)
Cheng et al. (2004)
Reference(s)
Naganathan et al.
(2008a)
Naganathan et al.
(2008b)
Peng and Wu (2008
Peng et al. (2009)
400–1,000
Wavelength range
(nm)
900–1,700
447–951
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Near-infrared Imaging and Spectroscopy
109
Technique
Fish
Fish
Interactance
Interactance
Product
Fish
Product type
Reflectance
Mode
Table 6.1 (continued)
Determination of
freshness
Determination of
moisture and fat
content in fillets
Determination of salt
and fat distribution
in fillets
Analysis
ElMasry and Wold
(2008)
Segtnan et al. (2009)
760–1,040
Chau et al. (2009)
Reference(s)
760–1,040
Wavelength range
(nm)
892–2,495
110
V. Chelladurai and D. S. Jayas
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Near-infrared Imaging and Spectroscopy
111
Fig. 6.13 Inframatic
9500 NIR grain analyser
(Courtesy: www. Perten.com)
feasibility of NIR techniques for protein and moisture content measurement, and
nowadays, most of the commercial grain handling facilities use NIR spectroscopy
for protein, moisture, and fat measurement of cereal grains (Delwiche 1995, 1998;
Gributs and Burns 2006; Mahesh et al. 2008; Miralbés 2004; Wang et al. 2004b).
Berardo et al. (2004) measured the carotenoid concentrations in maize by applying
NIR spectroscopy. A commercial grain analyser which is using NIR spectroscopic
technique is shown in Fig. 6.13.
The NIR spectroscopy successfully identified the internal damages caused
by most of the storage and field insects (Baker et al. 1999; Dowell et al. 1998;
Maghirang et al. 2003; Paliwal et al. 2004; Perez-Mendoza et al. 2003). Most
of the studies stated that 990, 1,135, 1,325, 1,370, 1,395, 1,425, 1,510, 1,610,
and 1,670 nm wavelengths were significant for insect damage identification.
Wavelength 990 nm relates to the loss of starch in the kernel consumed by developing insects; 1,510 nm relates to the change in protein content of infested grains;
and 1,335 and 1,670 nm represent the cuticular lipids of the insects (Ridgway et al.
1999). The waveband around 1,425 nm was identified by Ridgway and Chambers
(1998) corresponding to insect-related moisture. Mycotoxins, toxic chemical substances produced during metabolism of some fungal species pose health risk to
humans and animals when consumed. The NIR spectroscopy systems were tested
for prediction of mycotoxins in cereal grains. Classification models from NIR
reflectance and transmittance spectra classified corn kernels containing either high
(>100 ppb) or low (<10 ppb) levels of aflatoxin (Pearson et al. 2001), but could not
predict the exact concentration of aflatoxin. Fumonisin level in corn was detected
by NIR spectroscopy in the wavelength range 550–1,700 nm (Dowell et al. 2002).
112
V. Chelladurai and D. S. Jayas
The NIR spectroscopy was also used for the measurement of deoxynivalenol
(DON) level in barley and wheat (Pettersson and Åberg 2003; Ruan 2002) using
absorbance and transmittance modes.
Oilseeds
Prediction of oil content and moisture of oilseeds helps the processers during storage and oil extraction process. Introduction of NIR techniques helped the processers to reduce the quality analysis time and also to eliminate the subjectivity in the
analysis process. Ben-Gera and Norris (1968a) used spectrophotometry technique
to predict the moisture content of soybeans. Baianu et al. (2012) used a Fourier
transform-NIR spectroscopy technique to develop prediction model for oil and
protein content of soybeans and achieved higher correlation values (R > 0.99). Oil
content of cottonseed, groundnut or peanut, rapeseed (canola), safflower, flaxseed,
soybean, sunflower, sesame seed, and palm kernel were measured using NIR scanning monochromator-research composition analyser (RCA) and the wavelengths
around 2,310 nm were suitable for estimation of oil content (Panford and Deman
1990). The variation in fatty acid composition of oilseeds played a major role in
selection of exact wavelength for estimation of oil content in each type of oilseed.
The NIR instruments have also been used to detect the fungal damages in oilseeds
(Senthilkumar et al. 2012; Wang et al. 2002, 2004a).
Fruits and Vegetables
Adulteration of fruit purees and juices is a big concern to the food safety personnel
and consumers. The NIR spectroscopy can be used as a detection tool for adulteration in orange, apple, raspberry, and strawberry purees (Contal et al. 2002; Evans
et al. 1993; León et al. 2005; Reid et al. 2005; Scotter and Legrand 1995; Shildon
et al. 1998; Twomey et al. 2006).
Dairy Products
The strict regulatory rules for raw materials, processes, and final products in the
dairy industry and increase in interest of consumers towards quality products
made the dairy industry to use advance techniques like NIR spectroscopy for
quality analysis and process control. Goulden (1957) obtained the near-infrared
spectrum of lactose, casein, fat, and powdered milk using several wavelengths,
and Ben-Gera and Norris (1968c) used near-infrared spectrum for the first time
6
Near-infrared Imaging and Spectroscopy
113
to determine the components of milk using MLR technique. The moisture content of milk powder was measured while moving on a belt conveyor at 1,940 nm
(principal water absorption band) n 1981 (Rodriguez-Otero et al. 1997). From
then onwards, the NIR spectroscopy techniques have been tested for determination of moisture, fat, protein, and lactose in skim milk, and milk powders (Baer
et al. 1983; Downey et al. 1990; Kamishikiryo-Yamashita et al. 1994; Robert
et al. 1987) with the wavelengths of 1,724, 1,752, 2,308, and 2,344 nm related to
fat; 2,050 and 2,180 nm to protein; and 2,094 nm to lactose content. Adulteration
of milk products is a huge concern to the consumers because sometimes more
valuable components of milk are removed during adulteration. The NIR techniques have been used for detection of adulteration in milk and milk products. The
strange fat in the milk samples was detected by Sato et al. (1990) using NIR spectroscopy, and wavelength of 1,100–2,500 nm was used to detect soluble materials
in milk and milk powder (Giangiacomo et al. 1991; Pedretti et al. 1993). Maraboli
et al. (2002) developed calibration methods for accurate determination of quantity
of non-dairy protein isolates added to milk powder using NIR spectroscopy. The
quality parameters of cheese (fat, protein, and moisture contents) were determined
using NIR spectroscopy systems (Adamopoulos et al. 2001; Cattaneo et al. 2005;
Frank and Birth 1982; Frankhuizen 1992; Pierce and Wehling 1994). The FT-NIR
spectroscopy was used for classification of Emmental cheeses based on the geographical locations (Manley et al. 2008; Pillonel et al. 2003).
Meat and Meat Products
The application of NIR technique in component analysis of meat products
started in middle of twentieth century and Ben-Gera and Norris (1968b) used
it to determine the fat and moisture in the meat. Prieto et al. (2009) did an
elaborate review on application of NIR spectroscopy in meat and meat products. In 2007, AOAC officially approved NIR transmittance-mode spectroscopy instruments along with the ANN calibration model developed by FOSS
as a first action official method for commercial analysis of fat, moisture, and
protein in meat and meat products (Anderson 2007). Nowadays, there are different types of meat analyser units commercially available in the market to
analyse the chemical components of the meat in a single run (Fig. 6.14).
The NIR spectroscopic techniques were tested for measuring protein content at 1,460–1,570 nm and 2,000–2,180 nm due to the absorption by N-H
bands, intramuscular fat at 1,100–1,400, 1,700, and 2,200–2,400 nm due to the
absorption by C-H bonds of fatty acids, and moisture 1,450 and 1,940 nm due
to the absorption by O-H bands, in beef (Prieto et al. 2006), mutton (Viljoen
et al. 2007), poultry meat (Berzaghi et al. 2005; Rahim and Ghazali 2012;
McDevitt et al. 2005), and pork (Gaitán-Jurado et al. 2008). Tenderness of
the meat has been predicted using NIR spectroscopy (Jeyamkondan et al.
2003; Mitsumoto et al. 1991). The NIR spectroscopic techniques can also be
114
V. Chelladurai and D. S. Jayas
Fig. 6.14 FOSS MeatScan fat analyser (Courtesy:
www.Foss.dk)
used to classify beef and poultry meats based on the tenderness of the meat
(Meullenet et al. 2004; Park et al. 1998; Rødbotten et al. 2001), and ham based
on its texture and colour (Garcia-Rey et al. 2005). Visible and short-wave nearinfrared (SWNIR) spectroscopy successfully predicted the freshness of the
packaged chicken breasts (Grau et al. 2011) and proved that packaging film
did not affect the spectroscopic data. The NIR spectroscopy has the capacity to differentiate lamb and beef mixtures (Cozzolino et al. 2000; McElhinney
et al. 1999), kangaroo and beef meats (Ding and Xu 1999), fresh pork, turkey, and chicken (Rannou and Downey 1997), which helps to detect the meat
adulterations.
Table 6.2 shows the summary of applications of NIR spectroscopy in agricultural and food industry.
Conclusions
Near-infrared spectroscopy is now commercially used for measurement of moisture and other chemical components of the cereal grains and oilseeds in grain handling industry. Meat industry also started using NIR techniques for non-destructive
quality monitoring operations. The NIR instruments were also tested for in-line
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Cereal products
Wheat
Corn
Absorbance
Reflectance
Reflectance
Reflectance
Absorbance
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance
Absorbance
Reflectance
Reflectance
Transmittance and
reflectance
Product
Wheat
Product type
Cereal grains
Mode
Reflectance
Classification of vitreous and nonvitreous kernels
Identification of partially waxy and
wild wheat varieties
Classification of dark hard vitreous and non-dark hard vitreous
kernels
Measurement of adulteration in
durum wheat flour
Detection of insect infestations
Detection of heat-damaged kernels
Detection of mould and scab
damages
Detection of insect infestations
Detection of insect fragments
Percentage volume of flour particles
Prediction of protein content
Measurement of starch structure
and degree of processing
Measurement of protein content
Detection of aflatoxin levels
Classification of single kernels of
wheat
Application
Table 6.2 Application of NIR spectroscopy in agricultural and food industry
Reference(s)
1,100–2,498
550–1,700
400–1,700
550–1,700
400–2,500
740–1,139
1,100–2,500
400–1,700
400–1,700
940–1,700
400–2,498
400–1,700
1,100–2,498
(continued)
Delwiche (1998)
Pearson et al. (2001)
Maghirang et al. (2003)
Perez-Mendoza et al. (2003)
Hareland (1994)
Delwiche (1995)
Guy et al. (1996)
Baker et al. (1999)
Wang et al. (2001)
Delwiche (2003)
Cocchi et al. (2006)
Delwiche and Graybosch
(2002)
Wang et al. (2002)
Delwiche and Massie
551–750 (colour),
(1996)
1,120–2,476 (intrinsic
properties)
400–1,700
Dowell (2000)
Wavelength range (nm)
6
Near-infrared Imaging and Spectroscopy
115
Beef
Reflectance
Wheat
Beef
Absorbance
Reflectance
Beef
Wheat
Transmittance
Transmittance
Wheat
Transmittance
Meat and meat
products
Barley
Corn
Product
Wheat
Transmittance and
reflectance
Absorbance
Table 6.2 (continued)
Mode
Product type
Reflectance
Prediction of chemical components
(protein, IMF, moisture)
Prediction of pH and colour (L, a, b
values)
Prieto et al. (2008)
(continued)
Hoving-Bolink et al. (2005)
1,100–2,500
Tøgersen et al. (1999)
Cozzolino and Murray
(2002)
Alomar et al. (2003)
Prieto et al. (2006)
De Marchi et al. (2010)
Ripoll et al. (2008)
Anderson (2007)
Wang et al. (2004b)
Sanderson et al. (1997)
Miralbés (2004)
Pettersson and Åberg (2003)
Ruan (2002)
Dowell et al. (2002)
Reference(s)
Wesley et al. (2001)
1,000–1,700
400–2,500
1,100–2,500
1,100–2,498
400–2,500
850–1,050
Application
Wavelength range (nm)
Measurement of gliadin and glutenin 1,100–2,498
contents
Detection of fumonisin
550–1,050 (transmittance)
400–1,700 (reflectance)
Measurement of deoxynivalenol
400–2,500
(DON)
Measurement of deoxynivalenol
570–1,100
(DON)
Measurement of quality parameters
850–1,048
of wheat
Determination of moisture content
850–2,000
1,100–2,498
Prediction of chemical components
(protein, intermuscular fat (IMF),
moisture, ash)
1,441–1,810
400–2,500
116
V. Chelladurai and D. S. Jayas
Beef
Beef
Beef
Pork
Pork sausages
Pork
Lamb
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance
Reflectance
Beef
Beef
Reflectance
Reflectance
Product
Beef
Table 6.2 (continued)
Mode
Product type
Reflectance
Prediction of chemical components
(protein, IMF, moisture)
Prediction of sensory attributes (flavour, taste, firmness, marbling)
Prediction of pH and colour (L, a, b
values)
Prediction of chemical components
(protein, IMF, moisture)
Application
Prediction of sensory attributes (flavour, tenderness, texture)
Classification of frozen and unfrozen
beef
Differentiation of beef and Kangaroo
meat
Identification of spinal
cord-adulteration
Detection of hamburger adulteration
Differentiating cow meat from bull
meat
Prediction of chemical components
(protein, IMF, moisture)
400–2,498
400–800 (Visible)
802–2,500 (NIR)
400–1,700
400–2,500
400-2,500
515–1,650
1,441–1,810
802–2,500
400–2,500
400–2,500
400–2,500
400–2,500
1,100–2,500
1,000–1,950
400–2,500
Wavelength range (nm)
750–1,098
750–1,100
400–2,500
(continued)
Andres et al. (2007)
Ortiz-Somovilla et al.
(2007)
Brøndum et al. (2000)
Brøndum et al. (2000)
Chan et al. (2002)
Cozzolino et al. (2000)
Cozzolino and Murray
(2002)
Tøgersen et al. (1999)
Brøndum et al. (2000)
Barlocco et al. (2006)
Cozzolino et al. (2003)
Meulemans et al. (2002)
Ding and Xu (2000)
Rødbotten et al. (2000)
Gangidi et al. (2005)
Ding and Xu (1999)
Reference(s)
Byrne et al. (1998)
Venel et al. (2001)
Thyholt and Isaksson (1997)
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117
Product
Mutton
Lamb
Poultry
Poultry
Poultry
Poultry
Poultry
Table 6.2 (continued)
Mode
Product type
Reflectance
Reflectance
Reflectance
Reflectance
Transmittance
Reflectance
Reflectance
Prediction of sensory attributes
(flavour, juiciness, tenderness,
chewiness)
Prediction of fatty acid composition
Identification of broiler chicken
from local chickens
Classification of chicken breasts
(tough and tender)
Application
Prediction of chemical components
(protein, IMF, dry matter, ash)
Prediction of sensory attributes
(flavour, juiciness, texture)
Prediction of chemical components
(protein, IMF, moisture, ash)
400–1,850
400–2,500
850–1,050
400–2,500
1,308–2,388
1,100–2,498
1,100–2,500
400–1,080
400–2,498
Wavelength range (nm)
1,100–2,500
Liu et al. (2004)
Meullenet et al. (2004)
Riovanto et al. (2012)
Ding et al. (1999)
Abeni and Bergoglio (2001)
Berzaghi et al. (2005)
Viljoen et al. (2005)
Liu et al. (2004)
Andres et al. (2007)
Reference(s)
Viljoen et al. (2007)
118
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measurement of quality parameters of grains, fruits, and meat products. The ability
of hyperspectral imaging systems to combine spectral and spatial data of a sample
made this system a standalone unit for non-destructive analysis of chemical, physical, and textural parameters of the sample. The NIR hyperspectral imaging system
has been tested elaborately for chemical composition prediction, detection of defects
and adulteration of agricultural and food products. In spite of these interesting findings, the implementation of NIR hyperspectral imaging systems for in-line monitoring has been difficult due to the large size of hyperspectral data produced and
time needed for analysing these data. Identification of key wavelengths and development of multispectral imaging system based on the indented use will eliminate
these drawbacks. The other limitation of NIR hyperspectral imaging systems is the
need for standardized calibration methods and preprocessing techniques to eliminate errors such as dead pixels in the image, thermal drift, and optical errors. The
recent developments in hardware and software of NIR hyperspectral imaging systems to overcome the limitations of this technology will help the agricultural and
food industry in implementing the NIR hyperspectral imaging systems for rapid and
in-line quality monitoring applications such as foreign material detection, discrimination of agricultural and food products based on quality attributes and detection of
defects, diseases, and food adulteration.
References
Abeni F, Bergoglio G (2001) Characterization of different strains of broiler chicken by carcass
measurements, chemical and physical parameters and NIRS on breast muscle. Meat Science
57(2):133–137. doi:http://dx.doi.org/10.1016/S0309-1740(00)00084-X
Adamopoulos KG, Goula AM, Petropakis HJ (2001) Quality control during processing of feta
cheese—NIR application. J Food Compos Anal 14(4):431–440
Alomar D, Gallo C, Castañeda M, Fuchslocher R (2003) Chemical and discriminant analysis of
bovine meat by near infrared reflectance spectroscopy (NIRS). Meat Sci 63(4):441–450.
doi:http://dx.doi.org/10.1016/S0309-1740(02)00101-8
Anderson S (2007) Determination of fat, moisture, and protein in meat and meat products by
using the FOSS FoodScan near-infrared spectrophotometer with FOSS artificial neural network calibration model and associated database: collaborative study. J AOAC Int
90(4):1073–1083
Andrés S, Murray I, Navajas EA, Fisher AV, Lambe NR, Bünger L (2007) Prediction of sensory
characteristics of lamb meat samples by near infrared reflectance spectroscopy. Meat Sci
76(3):509–516. doi:http://dx.doi.org/10.1016/j.meatsci.2007.01.011
Ariana D, Lu R (2002) A near-infrared sensing technique for measuring internal quality of apple
fruit. Appl Eng Agric 18(5):585–592
Ariana DP, Lu R (2008) Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—part II. Performance of a prototype. Sens Instrum Food
Qual Saf 2(3):152–160
Ariana DP, Lu R, Guyer DE (2006) Near-infrared hyperspectral reflectance imaging for detection
of bruises on pickling cucumbers. Comput Electron Agric 53(1):60–70
Baer RJ, Frank JF, Loewenstein M, Birth GS (1983) Compositional analysis of whey powders
using near infrared diffuse reflectance spectroscopy. J Food Sci 48(3):959–961
Baianu I, You T, Costescu D, Lozano P, Prisecaru V, Nelson R (2012) Determination of soybean
oil, protein and amino acid residues in soybean seeds by high resolution nuclear magnetic
120
V. Chelladurai and D. S. Jayas
resonance (NMRS) and near Infrared (NIRS). http://dx.doi.org/10.1038/npre.2012.7053.1
Accessed 14 Dec 2012
Baker JE, Dowell FE, Throne JE (1999) Detection of parasitized rice weevils in wheat kernels
with near-infrared spectroscopy. Biol Control 16(1):88–90
Barlocco N, Vadell A, Ballesteros F, Galietta G, Cozzolino D (2006) Predicting intramuscular
fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance
spectroscopy. Anim Sci 82(1):111–116
Ben-Gera I, Norris KH (1968a) Determination of moisture content in soybeans by direct spectrophotometry. Isr J Agric Res 18(3):125–132
Ben-Gera I, Norris KH (1968b) Direct spectrophotometric determination of fat and moisture in
meat products. J Food Sci 33(1):64–67. doi:10.1111/j.1365-2621.1968.tb00885.x
Ben-Gera I, Norris KH (1968c) Influence of fat concentration on the absorption spectrum of milk
in the near-infrared region. Isr J Agric Res 18(3):117–124
Berardo N, Brenna O, Amato A, Valoti P, Pisacane V, Motto M (2004) Carotenoids concentration among maize genotypes measured by near infrared reflectance spectroscopy (NIRS).
Innovative Food Sci Emerg Technol 5(3):393–398
Bertrand D, Robert P, Loisel W (1985) Identification of some wheat varieties by near infrared
reflectance spectroscopy. J Sci Food Agric 36(11):1120–1124
Berzaghi P, Dalle Zotte A, Jansson LM, Andrighetto I (2005) Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between
different n-3 feeding sources. Poult Sci 84(1):128–136
Brøndum J, Munck L, Henckel P, Karlsson A, Tornberg E, Engelsen SB (2000) Prediction of
water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat
Sci 55(2):177–185. doi:http://dx.doi.org/10.1016/S0309-1740(99)00141-2
Byrne CE, Downey G, Troy DJ, Buckley DJ (1998) Non-destructive prediction of selected quality attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098 nm.
Meat Sci 49(4):399–409. doi:http://dx.doi.org/10.1016/S0309-1740(98)00005-9
Call J, Lodder RA (2002) Application of a liquid crystal tunable filter to near-infrared spectral
searches. Proc SETICon 02:18–22
Cattaneo TMP, Giardina C, Sinelli N, Riva M, Giangiacomo R (2005) Application of FT-NIR and
FT-IR spectroscopy to study the shelf-life of Crescenza cheese. Int Dairy J 15(6):693–700
Chan DE, Walker PN, Mills EW (2002) Prediction of pork quality characteristics using visible
and near-infrared spectroscopy. Trans ASAE 45(5):1519–1527
Chau A, Whitworth M, Leadley C, Millar S (2009) Innovative sensors to rapidly and nondestructively determine fish freshness. Seafish Industrial Authority
Cheng X, Chen YR, Tao Y, Wang CY, Kim MS, Lefcourt AM (2004) A novel integrated PCA
and FLD method on hyperspectral image feature extraction for cucumber chilling damage
inspection. Trans ASAE 47(4):1313–1320
Choudhary R, Mahesh S, Paliwal J, Jayas DS (2009) Identification of wheat classes using wavelet
features from near infrared hyperspectral images of bulk samples. Biosyst Eng 102(2):115–127
Cocchi L, Vescovi L, Petrini LE, Petrini O (2006) Heavy metals in edible mushrooms in Italy.
Food Chem 98(2):277–284
Cogdill RP, Hurburgh CR, Rippke GR (2004) Single-kernel maize analysis by near-infrared
hyperspectral imaging. Trans ASAE 47(1):311–320
Contal L, Leon V, Downey G (2002) Detection and quantification of apple adulteration in strawberry and raspberry purées using visible and near infrared spectroscopy. J Near Infrared
Spectrosc 10(4):289–300
Cozzolino D, Barlocco N, Vadell A, Ballesteros F, Gallieta G (2003) The use of visible and nearinfrared reflectance spectroscopy to predict colour on both intact and homogenised pork
muscle. LWT—Food Science and Technology 36(2):195–202. doi:http://dx.doi.org/10.1016/
S0023-6438(02)00199-8
Cozzolino D, Murray I (2002) Effect of sample presentation and animal muscle species on
the analysis of meat by near infrared reflectance spectroscopy. J Near Infrared Spectrosc
10(1):37–44
6
Near-infrared Imaging and Spectroscopy
121
Cozzolino D, Murray I, Scaife J, Paterson R (2000) Study of dissected lamb muscles by visible and
near infrared reflectance spectroscopy for composition assessment. Anim Sci 70(3):417–423
De Marchi M, Berzaghi P, Boukha A, Mirisola M, Gallo L (2010) Use of near infrared spectroscopy
for assessment of beef quality traits. Ital J Anim Sci 6(1):421–423
Delwiche SR (1995) Single wheat kernel analysis by near-infrared transmittance: protein content.
Cereal Chem 72(1):11–16
Delwiche SR (1998) Protein content of single kernels of wheat by near-infrared reflectance spectroscopy. J Cereal Sci 27(3):241–254
Delwiche SR (2003) Classification of scab-and other mold-damaged wheat kernels by near-infrared
reflectance spectroscopy. Trans ASAE 46(3):731–738
Delwiche SR, Chen Y-R, Hruschka WR (1995) Differentiation of hard red wheat by near-infrared
analysis of bulk samples. Cereal Chem 72(3):243–247
Delwiche SR, Graybosch RA (2002) Identification of waxy wheat by near-infrared reflectance
spectroscopy. J Cereal Sci 35(1):29–38
Delwiche SR, Massie DR (1996) Classification of wheat by visible and near-infrared reflectance
from single kernels. Cereal Chem 73(3):399–405
Delwiche SR, Norris KH (1993) Classification of hard red wheat by near-infrared diffuse reflectance spectroscopy. Cereal Chem 70(1):29
Ding H, Xu RJ, Chan DKO (1999) Identification of broiler chicken meat using a visible/nearinfrared spectroscopic technique. J Sci Food Agric 79(11):1382–1388
Ding HB, Xu RJ (1999) Differentiation of beef and kangaroo meat by visible/near-infrared reflectance spectroscopy. J Food Sci 64(5):814–817. doi:10.1111/j.1365-2621.1999.tb15918.x
Ding HB, Xu RJ (2000) Near-infrared spectroscopic technique for detection of beef hamburger
Adulteration. J Agric Food Chem 48(6):2193–2198. doi:10.1021/jf9907182
Dowell FE (1997) Effect of NaOH on visible wavelength spectra of single wheat kernels and
color classification efficiency. Cereal Chem 74(5):617–620
Dowell FE (2000) Differentiating vitreous and nonvitreous durum wheat kernels by using nearinfrared spectroscopy. Cereal Chem 77(2):155–158
Dowell FE, Pearson TC, Maghirang EB, Xie F, Wicklow DT (2002) Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with
Fusarium verticillioides. Cereal Chem 79(2):222–226
Dowell FE, Throne JE, Baker JE (1998) Automated nondestructive detection of internal insect
infestation of wheat kernels by using near-infrared reflectance spectroscopy. J Econ
Entomol 91(4):899–904
Downey G (1986) Development, evaluation and collaborative testing of calibrations for the prediction of protein and moisture in ground barley by near infra-red reflectance. Ir J Food Sci
Technol 10:119–126
Downey G, Robert P, Bertrand D, Kelly PM (1990) Classification of commercial skim milk powders according to heat treatment using factorial discriminant analysis of near-infrared reflectance spectra. Appl Spectrosc 44(1):150–155
Ellis JW, Bath J (1938) Modifications in the near infra-red absorption spectra of protein
and of light and heavy water molecules when water is bound to gelatin. J Chem Phys
6(11):723–729
ElMasry G, Barbin DF, Sun DW, Allen P (2012) Meat quality evaluation by hyperspectral imaging technique: an overview. Crit Rev Food Sci Nutr 52(8):689–711. doi:10.1080/10408398.
2010.507908
ElMasry G, Wang N, ElSayed A, Ngadi M (2007) Hyperspectral imaging for nondestructive
determination of some quality attributes for strawberry. J Food Eng 81(1):98–107
ElMasry G, Wold JP (2008) High-speed assessment of fat and water content distribution in fish
fillets using online imaging spectroscopy. J Agric Food Chem 56(17):7672–7677
Evans D, Scotter C, Day L, Hall M (1993) Determination of the authenticity of orange juice by
discriminant analysis of near infrared spectra. J Near Infrared Spectrosc 1:33–44
Frank JF, Birth GS (1982) Application of near infrared reflectance spectroscopy to cheese analysis.
J Dairy Sci 65(7):1110–1116
122
V. Chelladurai and D. S. Jayas
Frankhuizen R (1992) NIR analysis of dairy products. Pract Spectrosc Ser 13:609
Gaitán-Jurado AJ, Ortiz-Somovilla V, España-España F, Pérez-Aparicio J, De Pedro-Sanz EJ
(2008) Quantitative analysis of pork dry-cured sausages to quality control by NIR spectroscopy. Meat Sci 78(4):391–399
Gangidi RR, Proctor A, Pohlman FW, Meullenet J-F (2005) Rapid determination of spinal
cord content in ground beef by near-infrared spectroscopy. J Food Sci 70(6):c397–c400.
doi:10.1111/j.1365-2621.2005.tb11436.x
Garcia-Rey RM, Garcia-Olmo J, De Pedro E, Quiles-Zafra R, de Castro Luque MD (2005)
Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy
using a fiber optic probe. Meat Sci 70(2):357–363
Gat N (2000) Imaging spectroscopy using tunable filters: a review. In: AeroSense 2000.
International society for optics and photonics, pp 50–64
Giangiacomo R, Braga F, Galliena C (1991) Use of near-infrared spectroscopy to detect whey
powder mixed with milk powder. In: Murray I, Cowe IA (eds) Making light work: advances
in near-infrared spectroscopy. VCH, Weinheim, pp 399–407
Gorretta N, Roger JM, Aubert M, Bellon-Maurel V, Campan F, Roumet P (2006) Determining
vitreousness of durum wheat kernels using near infrared hyperspectral imaging. J Near
Infrared Spectrosc 14(4):231–239
Goulden JDS (1957) 676. Diffuse reflexion spectra of dairy products in the near infra-red region.
J Dairy Res 24(02):242–251. doi:10.1017/S0022029900008785
Gowen AA, O’Donnell CP, Cullen PJ, Downey G, Frias JM (2007) Hyperspectral imaging—
an emerging process analytical tool for food quality and safety control. Trends Food Sci
Technol 18(12):590–598
Gowen AA, Taghizadeh M, O’Donnell CP (2009) Identification of mushrooms subjected to
freeze damage using hyperspectral imaging. J Food Eng 93(1):7–12
Grau R, Sánchez AJ, Girón J, Iborra E, Fuentes A, Barat JM (2011) Nondestructive assessment
of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy. Food Res Int
44(1):331–337
Gributs CEW, Burns DH (2006) Parsimonious calibration models for near-infrared spectroscopy
using wavelets and scaling functions. Chemometr Intell Lab Syst 83(1):44–53
Guy RCE, Osborne BG, Robert P (1996) The application of near infrared reflectance spectroscopy
to measure the degree of processing in extrusion cooking processes. J Food Eng 27(3):241–258
Hareland GA (1994) Evaluation of flour particle size distribution by laser diffraction, sieve analysis and near-infrared reflectance spectroscopy. J Cereal Sci 20(2):183–190
Hart JR, Norris KH, Golumbic C (1962) Determination of the moisture content of seeds by nearinfrared spectrophotometry of their methanol extracts. Cereal Chem 39(2):94–99
Headwall (2012) Spectral imaging capabilities of hyperspec™ imaging technology in pharmaceutical operations. http://www.headwallphotonics.com/downloads/hw_hyperspectral-in-pharma.
pdf. Accessed 12 Dec 2012
Hildrum KI, Nilsen BN, Westad F, Wahlgren NM (2004) In-line analysis of ground beef using a
diode array near infrared instrument on a conveyor belt. J Near Infrared Spectrosc 12:367–376
Hindle PH (2008) Historical development. In: Burns DA, Ciurczak EW (eds) Handbook of nearinfrared analysis, vol 35. CRC, Boca Raton, pp 3–6
Hoving-Bolink AH, Vedder HW, Merks JWM, de Klein WJH, Reimert HGM, Frankhuizen
R, van den Broek WHAM, Lambooij eE (2005) Perspective of NIRS measurements
early post mortem for prediction of pork quality. Meat Sci 69(3):417–423. doi:http://dx.
doi.org/10.1016/j.meatsci.2004.08.012
Hruschka WR (1987) Data analysis: wavelength selection methods. In: Williams P, Norris KH
(eds) Near-infrared technology in the agricultural and food industries, vol 2., AACCSt. Paul,
Minnesota, pp 39–58
Jayas DS, Singh CB, Paliwal J (2010) Classification of wheat kernels using near-infrared reflectance hyperspectral imaging. In: Sun D-W (ed) Hyperspectral imaging for food quality analysis and control, 1st edn. Academic Press, London, pp 449–470
6
Near-infrared Imaging and Spectroscopy
123
Jeyamkondan S, Kranzler GA, Morgan BJ, Rust S (2003) Predicting beef tenderness using
near-infrared spectroscopy. Proc SPIE 2003:356–365
Kamishikiryo-Yamashita H, Oritani Y, Takamura H, Matoba T (1994) Protein content in milk by
near-infrared spectroscopy. J Food Sci 59(2):313–315
Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2011) Application of NIR hyperspectral
imaging for discrimination of lamb muscles. J Food Eng 104(3):332–340. doi:http://dx.
doi.org/10.1016/j.jfoodeng.2010.12.024
Kamruzzaman M, Sun D-W, ElMasry G, Allen P (2012) Fast detection and visualization of
minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image
analysis. Talanta 103:130–136
Kaye W (1954) Near-infrared spectroscopy: I. Spectral identification and analytical applications.
Spectrochim Acta 6(4):257–287. doi:http://dx.doi.org/10.1016/0371-1951(54)80011-7
Kim MS, Chen YR, Mehl PM (2001) Hyperspectral reflectance and fluorescence imaging system
for food quality and safety. Trans ASAE 44(3):721–729
Kim MS, Lefcourt AM, Chao K, Chen YR, Kim I, Chan DE (2002) Multispectral detection of
fecal contamination on apples based on hyperspectral imagery: part I. Application of visible
and near-infrared reflectance imaging. Trans ASAE 45(6):2027–2038
Lawrence KC, Windham WR, Park B, Smith DP, Poole GH (2003) Comparison between
visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on
poultry carcasses. In: Conference on monitoring food safety, Agriculture, and Plant Health.
Providence, Rhode Islands, pp 35–42
Lee K-J, Kang S, Kim MS, Noh SH (2005). Hyperspectral imaging for detecting defect on
apples. In: 2005 ASAE annual international meeting, Tampa, Florida, Paper no 053075,
17–20 July 2005
Lefcout AM, Kim MS, Chen Y-R, Kang S (2006) Systematic approach for using hyperspectral
imaging data to develop multispectral imagining systems: detection of feces on apples.
Comput Electron Agric 54(1):22–35
León L, Kelly JD, Downey G (2005) Detection of apple juice adulteration using near-infrared
transflectance spectroscopy. Appl Spectrosc 59(5):593–599
Liu Y, Chen Y-R, Kim MS, Chan DE, Lefcourt AM (2007) Development of simple algorithms for
the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging. J Food Eng 81(2):412–418
Liu Y, Lyon BG, Windham WR, Lyon CE, Savage EM (2004) Prediction of physical, color, and
sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy.
Poult Sci 83(8):1467–1474
Lu R (2003) Detection of bruises on apples using near-infrared hyperspectral imaging. Trans
ASAE 46(2):523–530
Lu R, Peng Y (2006) Hyperspectral scattering for assessing peach fruit firmness. Biosyst Eng
93(2):161–171
Maghirang EB, Dowell FE (2003) Hardness measurement of bulk wheat by single-kernel visible
and near-infrared reflectance spectroscopy. Cereal Chem 80(3):316–322
Maghirang EB, Dowell FE, Baker JE, Throne JE (2003) Automated detection of single wheat
kernels containing live or dead insects using near-infrared reflectance spectroscopy. Trans
ASAE 46(4):1277–1284
Mahesh S, Jayas DS, Paliwal J, White NDG (2011) Identification of wheat classes at different
moisture levels using near-infrared hyperspectral images of bulk samples. Sens Instrum
Food Qual Saf 5(1):1–9
Mahesh S, Manickavasagan A, Jayas DS, Paliwal J, White NDG (2008) Feasibility of nearinfrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst Eng
101(1):50–57
Manickavasagan A, Ganeshmoorthy K (2013) Total soluble solid (TSS) measurement in dates
at tamr stage using NIR reflectance imaging. In: CSBE/SCGAB 2013 annual conference,
Sakatoon, SK, Canada. Paper No. CSBE13-003, 7–10 July 2013
124
V. Chelladurai and D. S. Jayas
Manley M, Downey G, Baeten V (2008) Spectroscopic technique: near-infrared (NIR) spectroscopy.
In: Sun DW (ed) Modern Techniques for Food Authentication, 1st edn. Academic Press, New
York, pp 65–115
Maraboli A, Cattaneo TMP, Giangiacomo R (2002) Detection of vegetable proteins from soy, pea
and wheat isolates in milk powder by near infrared spectroscopy. J Near Infrared Spectrosc
10(1):63–70
McCarthy WJ, Kemeny GJ (2008) Fourier transform spectrophotometers in the near-infrared.
Pract Spectrosc Ser 35:79
McClure WF (2003) 204 years of near infrared technology: 1800–2003. J Near Infrared
Spectrosc 11(6):487–518
McDevitt RM, Gavin AJ, Andrés S, Murray I (2005) The ability of visible and near infrared reflectance
spectroscopy to predict the chemical composition of ground chicken carcasses and to discriminatebetween carcasses from different genotypes. J Near Infrared Spectrosc 13(3):109–117
McElhinney J, Downey G, O’Donnell C (1999) Quantitation of lamb content in mixtures with raw
minced beef using visible, near and mid-infrared spectroscopy. J Food Sci 64(4):587–591
Mehl PM, Chen Y-R, Kim MS, Chan DE (2004) Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J Food Eng 61(1):67–81
Meulemans A, Dotreppe O, Leroy B, Istasse L, Clinquart A (2002) Prediction of organoleptic and technological characteristics of pork meat by near infrared spectroscopy. Sci des Aliments 23:159–162
Meullenet J-F, Jonville E, Grezes D, Owens CM (2004) Prediction of the texture of cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat. J Texture
Stud 35(6):573–585. doi:10.1111/j.1538-7836.2004.01165.x-i1
Miller CE (2001) Chemical principles of near infrared technology. In: Williams P, Norris K (eds)
Near infrared technology in the agricultural and food industries. American Association of
Cereal Chemists Inc, St. Paul, Minnesota, pp 19–37
Miralbés C (2004) Quality control in the milling industry using near infrared transmittance spectroscopy. Food Chem 88(4):621–628
Mitsumoto M, Maeda S, Mitsuhashi T, Ozawa S (1991) Near-Infrared spectroscopy determination of physical and chemical characteristics in beef cuts. J Food Sci 56(6):1493–1496
Mohan LA, Karunakaran C, Jayas DS, White NDG (2005) Classification of bulk cereals using
visible and NIR reflectance characteristics. Can Biosyst Eng 47(7):7–14
Murray I, Williams PC (1987) Chemical principles of near-infrared technology. In: Williams P,
Norris K (eds) Near infrared technology in the agricultural and food industries, 1st edn.
American Association of Cereal Chemists Inc, St. Paul, Minnesota
Naganathan GK, Grimes LM, Subbiah J, Calkins CR, Samal A, Meyer GE (2008a) Partial least
squares analysis of near-infrared hyperspectral images for beef tenderness prediction. Sens
Instrum Food Qual Saf 2(3):178–188
Naganathan GK, Grimes LM, Subbiah J, Calkins CR, Samal A, Meyer GE (2008b) Visible/
near-infrared hyperspectral imaging for beef tenderness prediction. Comput Electron Agric
64(2):225–233
Nagata M, Tallada JG, Kobayashi T, Cui Y, Gejima Y (2004) Predicting maturity quality
parameters of strawberries using hyperspectral imaging. In: Paper presented at the 2004
ASAE/CSAE annual international meeting, Ottowa, Ontario, 1–4 Aug 2004
Nagata M, Tallada JG, Kobayashi T, Toyoda H (2005) NIR hyperspectral imaging for measurement of internal quality in strawberries. In: Paper presented at the 2005 ASAE annual international meeting, Tampa, Florida, 17–20 July 2005
Nakariyakul S, Casasent DP (2008) Hyperspectral waveband selection for contaminant detection
on poultry carcasses. Opt Eng 47(8):087202–087209
Nicolaï BM, Lötze E, Peirs A, Scheerlinck N, Theron KI (2006) Non-destructive measurement of
bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol Technol 40(1):1–6
Noh HK, Lu R (2007) Hyperspectral laser-induced fluorescence imaging for assessing apple fruit
quality. Postharvest Biol Technol 43(2):193–201
Norris KH, Barnes RF, Moore JE, Shenk JS (1976) Predicting forage quality by infrared reflectance spectroscopy. J Anim Sci 43(4):889–897
6
Near-infrared Imaging and Spectroscopy
125
Norris KH, Hart JR (1965) Direct spectroscopic determination of moisture content of grain and
seeds. In: International symposium on humidity and moisture, Washington, 1963. Reinhold
New York
Ortiz-Somovilla V, España-España F, Gaitán-Jurado AJ, Pérez-Aparicio J, De Pedro-Sanz EJ
(2007) Proximate analysis of homogenized and minced mass of pork sausages by NIRS.
Food Chem 101(3):1031–1040. doi:http://dx.doi.org/10.1016/j.foodchem.2006.02.058
Osborne BG, Fearn T, Hindle PH (1993) Theory of near-infrared spectrometry. In: Osborne B,
Fearn T, Hindle P (eds) Near infrared spectroscopy in food analysis. Longman Singapore
Publishers, Singapore
Paliwal J, Wang W, Symons SJ, Karunakaran C (2004) Insect species and infestation level determination in stored wheat using near-infrared spectroscopy. Can Biosyst Eng 46(7):17–24
Panford JA, Deman JM (1990) Determination of oil content of seeds by NIR: influence of fatty
acid composition on wavelength selection. J Am Oil Chem Soc 67(8):473–482
Park B, Chen YR, Hruschka WR, Shackelford SD, Koohmaraie M (1998) Near-infrared reflectance analysis for predicting beef longissimus tenderness. J Anim Sci 76(8):2115–2120
Pearson TC, Wicklow DT, Maghirang EB, Xie F, Dowell FE (2001) Detecting aflatoxin in single
corn kernels by transmittance and reflectance spectroscopy. Trans ASAE 44(5):1247–1254
Pedretti N, Bertrand D, Semenou M, Robert P, Giangiacomo R (1993) Application of an experimental
design to the detection of foreign substances in milk. J Near Infrared Spectrosc 1:174–184
Peirs A, Scheerlinck N, De Baerdemaeker J, Nicolai BM (2003) Starch index determination of
apple fruit by means of a hyperspectral near infrared reflectance imaging system. J Near
Infrared Spectrosc 11(5):379–390
Peiris KHS, Pumphery MO, Dowell FE (2009) NIR absorbance characteristics of deoxynivalenol and
of sound and fusarium—damaged wheat kernels. J Near Infrared Spectrosc 17(4):213–221
Peng Y, Wu J (2008) Hyperspectral scattering profiles for prediction of beef tenderness. In: Paper
presented at the 2008 ASABE annual international meeting, Providence, Rhode Island
Peng Y, Zhang J, Wu J, Hang H, Kim M, Tu S, Chao K (2009) Hyperspectral scattering profiles
for prediction of the microbial spoilage of beef. In: Sensing for agriculture and food quality
and safety, Orlando, Florida 2009. SPIE, Bellingham, Washington pp Q73150–Q73112
Perez-Mendoza J, Throne JE, Dowell FE, Baker JE (2003) Detection of insect fragments in
wheat flour by near-infrared spectroscopy. J Stored Prod Res 39(3):305–312
Pettersson H, Åberg L (2003) Near infrared spectroscopy for determination of mycotoxins in
cereals. Food Control 14(4):229–232
Pierce MM, Wehling RL (1994) Comparison of sample handling and data treatment methods for
determining moisture and fat in Cheddar cheese by near-infrared spectroscopy. J Agric Food
Chem 42(12):2830–2835
Pillonel L, Luginbühl W, Picque D, Schaller E, Tabacchi R, Bosset J (2003) Analytical methods
for the determination of the geographic origin of Emmental cheese: mid-and near-infrared
spectroscopy. Eur Food Res Technol 216(2):174–178
Polder G, Van Der Heijden GWA, Waalwijk C, Young IT (2005) Detection of Fusarium in single
wheat kernels using spectral imaging. Sci Technol 33(3):655–668
Prieto N, Andrés S, Giráldez FJ, Mantecón AR, Lavín P (2006) Potential use of near infrared
reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat
samples. Meat Sci 74(3):487–496. doi:http://dx.doi.org/10.1016/j.meatsci.2006.04.030
Prieto N, Andrés S, Giráldez FJ, Mantecón AR, Lavín P (2008) Ability of near infrared reflectance
spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle
meat samples. Meat Sci 79(4):692–699. doi:http://dx.doi.org/10.1016/j.meatsci.2007.10.035
Prieto N, Roehe R, Lavín P, Batten G, Andrés S (2009) Application of near infrared reflectance
spectroscopy to predict meat and meat products quality: a review. Meat Sci 83(2):175–186
Qiao J, Ngadi MO, Wang N, Gariépy C, Prasher SO (2007) Pork quality and marbling level
assessment using a hyperspectral imaging system. J Food Eng 83(1):10–16
Rahim HA, Ghazali R (2012) The application of near-infrared spectroscopy for poultry meat
grading. In: 2012 IEEE 8th international colloquium on signal processing and its applications (CSPA), Melaka, Malaysia, pp 58–62
126
V. Chelladurai and D. S. Jayas
Rannou H, Downey G (1997) Discrimination of raw pork, chicken and turkey meat by spectroscopy in the visible, near-and mid-infrared ranges. Anal Commun 34(12):401–404
Reid LM, Woodcock T, O’Donnell CP, Kelly JD, Downey G (2005) Differentiation of apple juice
samples on the basis of heat treatment and variety using chemometric analysis of MIR and
NIR data. Food Res Int 38(10):1109–1115
Ridgway C, Chambers J (1998) Detection of insects inside wheat kernels by NIR imaging. J
Near Infrared Spectrosc 6(1):115–120
Ridgway C, Chambers J, Cowe IA (1999) Detection of grain weevils inside single wheat kernels
by a very near infrared two-wavelength model. J Near Infrared Spectrosc 7(4):213–222
Riovanto R, De Marchi M, Cassandro M, Penasa M (2012) Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat. Food Chem 134(4):
2459–2464. doi:http://dx.doi.org/10.1016/j.foodchem.2012.04.038
Ripoll G, Albertí P, Panea B, Olleta JL, Sañudo C (2008) Near-infrared reflectance spectroscopy
for predicting chemical, instrumental and sensory quality of beef. Meat Sci 80(3):697–702.
doi:http://dx.doi.org/10.1016/j.meatsci.2008.03.009
Robert P, Bertrand D, Devaux MF, Grappin R (1987) Multivariate analysis applied to near-infrared spectra of milk. Anal Chem 59(17):2187–2191
Rødbotten R, Mevik B-H, Hildrum KI (2001) Prediction and classification of tenderness
in beef from non-invasive diode array detected NIR spectra. J Near Infrared Spectrosc
9(3):199–210
Rødbotten R, Nilsen BN, Hildrum KI (2000) Prediction of beef quality attributes from early post mortem near infrared reflectance spectra. Food Chem 69(4):427–436 doi:http://dx.doi.org/10.1016/
S0308-8146(00)00059-5
Rodriguez-Otero JL, Hermida M, Centeno J (1997) Analysis of dairy products by near-infrared
spectroscopy: a review. J Agric Food Chem 45(8):2815–2819
Ruan R (2002) Non-destructive determination of deoxynivalenol levels in barley using nearinfrared spectroscopy. Appl Eng Agric 18(5):549–554
Ruan R, Li Y, Lin X, Chen P (2002) Non-destructive determination of deoxynivalenol levels in
barley using near-infrared spectroscopy. Appl Eng Agric 18(5):549–553
Sanderson R, Lister SJ, Dhanoa MS, Barnes RJ, Thomas C (1997) Use of near infrared reflectance spectroscopy to predict and compare the composition of carcass samples from young
steers. Anim Sci 65(01):45–54. doi:10.1017/S1357729800016283
Sato T, Kawano S, Iwamoto M (1990) Detection of foreign fat adulteration of milk fat by near
infrared spectroscopic method. J Dairy Sci 73(12):3408–3413
Scotter CNG, Legrand A (1995) Near-infrared (NIR) spectroscopy as a screening technique for
fruit juice verification. Fruit Process 5:255–260
Segtnan VH, Høy M, Sørheim O, Kohler A, Lundby F, Wold JP, Ofstad R (2009) Noncontact salt
and fat distributional analysis in salted and smoked salmon fillets using X-ray computed
tomography and NIR interactance imaging. J Agric Food Chem 57(5):1705–1710
Senthilkumar T, Singh CB, Jayas DS, White NDG (2012) Detection of fungal infection in canola
using near-infrared hyperspectral imaging. J Agric Eng 49(1):21–27
Shahin M, Symons S (2008) Detection of hard vitreous and starchy kernels in amber durum
wheat samples using hyperspectral imaging. NIR News 19(5):16–18
Shilton N, Downey G, McNulty P (1998) Detection of orange juice adulteration by near-infrared
spectroscopy. Seminars in food analysis, 1998. Chapman & Hall, London, pp 155–162
Singh C, Jayas DS, Paliwal J, White N (2009a) Detection of insect-damaged wheat kernels using
near-infrared hyperspectral imaging. J Stored Prod Res 45(3):151–158
Singh CB, Jayas DS, Paliwal J, White NDG (2009b) Detection of sprouted and midge-damaged
wheat kernels using near-infrared hyperspectral imaging. Cereal Chem 86(3):256–260
Slaughter DC, Norris KH, Hruschka WR (1992) Quality and classification of hard red wheat.
Cereal Chem 69(4):7423–7432
Thyholt K, Isaksson T (1997) Differentiation of frozen and unfrozen beef using near-infrared spectroscopy. J Sci Food Agric 73(4):525–532. doi:10.1002/(sici)1097-0010(199704)73:4<525:
aid-jsfa767>3.0.co;2-c
6
Near-infrared Imaging and Spectroscopy
127
Tøgersen G, Isaksson T, Nilsen BN, Bakker EA, Hildrum KI (1999) On-line NIR analysis of
fat, water and protein in industrial scale ground meat batches. Meat Sci 51(1):97–102.
doi:http://dx.doi.org/10.1016/S0309-1740(98)00106-5
Tran CD (2003) Infrared multispectral imaging: principles and instrumentation. Appl Spectrosc
Rev 38(2):133–153
Twomey M, Downey G, McNulty PB (2006) The potential of NIR spectroscopy for the detection
of the adulteration of orange juice. J Sci Food Agric 67(1):77–84
Venel C, Mullen AM, Downey G, Troy D (2001) Prediction of tenderness and other quality
attributes of beef by near infrared reflectance spectroscopy between 750 and 1100 nm; further studies. J Near Infrared Spectrosc 9(3):185–198
Viljoen M, Hoffman L, Brand T (2005) Prediction of the chemical composition of freeze
dried ostrich meat with near infrared reflectance spectroscopy. Meat Sci 69(2):255–261.
doi:http://dx.doi.org/10.1016/j.meatsci.2004.07.008
Viljoen M, Hoffman L, Brand T (2007) Prediction of the chemical composition of mutton with
near infrared reflectance spectroscopy. Small Ruminant Res 69(1):88–94
Wang D, Dowell FE, Chung DS (2001) Assessment of heat-damaged wheat kernels using nearinfrared spectroscopy. Cereal Chem 78(5):625–628
Wang D, Dowell FE, Dempster R (2002) Determining vitreous subclasses of hard red spring
wheat using visible/near-infrared spectroscopy. Cereal Chem 79(3):418–422
Wang D, Dowell FE, Ram MS, Schapaugh WT (2004a) Classification of fungal-damaged soybean seeds using near-infrared spectroscopy. Int J Food Prop 7(1):75–82
Wang W, Pailwal J, Jayas DS (2004) Determination of moisture content of ground wheat using
near-infrared spectroscopy. In: Paper presented at the 2004 ASAE/CSAE annual international conference, Ottowa, Ontario, 1–4 Aug 2004
Wang W, Paliwal J (2007) Near-infrared spectroscopy and imaging in food quality and safety.
Sens Instrum Food Qual Saf 1(4):193–207
Wesley IJ, Larroque O, Osborne BG, Azudin N, Allen H, Skerritt JH (2001) Measurement of
gliadin and glutenin content of flour by NIR spectroscopy. J Cereal Sci 34(2):125–133
Williams P, Geladi P, Fox G, Manley M (2009) Maize kernel hardness classification by near infrared
(NIR) hyperspectral imaging and multivariate data analysis. Anal Chim Acta 653(2):121–130
Workman JJ, Burns DA (2001) Commercial NIR instrumentation. Pract Spectrosc Ser 27:53–70
Xing J, Bravo C, Jancsók PT, Ramon H, De Baerdemaeker J (2005) Detecting bruises on
‘Golden Delicious’ apples using hyperspectral imaging with multiple wavebands. Biosyst
Eng 90(1):27–36
Xing J, Van Hung P, Symons S, Shahin M, Hatcher D (2009) Using a short wavelength infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual
Canadian western wheat kernels. Sens Instrum Food Qual Saf 3(4):211–218
Yang C-C, Chao K, Kim MS (2009) Machine vision system for online inspection of freshly
slaughtered chickens. Sens Instrum Food Qual Saf 3(1):70–80
Yoon SC, Lawrence KC, Smith DP, Park B, Windham WR (2006) Bone fragment detection in
chicken breast fillets using diffuse scattering patterns of back-illuminated structured light.
In: Optics East 2006. International society for optics and photonics, pp 63810G–63810G
Yoon SC, Lawrence KC, Smith DP, Park B, Windham WR (2008) Embedded bone fragment
detection in chicken fillets using transmittance image enhancement and hyperspectral reflectance imaging. Sens Instrum Food Qual Saf 2(3):197–207
Zhang H, Paliwal J, Jayas DS, White NDG (2007) Classification of fungal infected wheat kernels
using near-infrared reflectance hyperspectral imaging and support vector machine. Trans
ASABE 50(5):1779–1785
Chapter 7
Mid- and Far-infrared Imaging
Sindhuja Sankaran, Lav R. Khot and Reza Ehsani
Introduction
The mid- and far-infrared spectra include wavelengths from 3 µm to 1 mm. This
spectral band contains lower radiation energy than visible spectra. The midinfrared (MIR) spectroscopy has shown great potential in identifying chemical
composition of the plant materials and food products. Similarly, another sensing technique that has garnered wide interest in recent years is terahertz technology. The terahertz frequencies (0.1–10 THz) are sensitive to moisture content and
applicable in the analysis of biological samples. In this chapter, both of these techniques and their applications in food and agriculture are discussed.
Mid-infrared Imaging
Any compound can be identified using MIR sensing, if it is infrared active. The
infrared-active compounds exhibit dipole movement during vibration. Molecular
vibration refers to periodic motion of atoms in a molecule. The vibrational modes
are stretching (symmetric/asymmetric stretching), bending (scissoring/rocking),
wagging, and twisting (Fig. 7.1).
S. Sankaran (*) · L. R. Khot
Department of Biological Systems Engineering, Washington State University,
64120, Pullman, WA 99164, USA
e-mail: sindhuja.sankaran@wsu.edu
R. Ehsani
Citrus Research and Education Center/IFAS, University of Florida,
700 Experiment Station Road, Lake Alfred, FL 33850, USA
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_7, © Springer-Verlag Berlin Heidelberg 2014
129
S. Sankaran et al.
130
Symmetrical stretching
Asymmetrical
stretching
Scissoring
Rocking
Wagging
Twisting
Fig. 7.1 Forms of molecular vibrations
The vibrational modes of a specific bond display spectral peaks in certain region of
the MIR spectra, thus allowing the qualitative and quantitative analysis of samples. The
spectral range in the MIR region is also often represented by wavenumber, where wavenumber is the reciprocal of wavelength in cm. The wavenumber of a particular vibrational movement depends on the bond strength and atomic mass. The molecules with
larger dipole movement have better absorption intensity. In general, stronger bonds will
vibrate at higher wavenumbers than weaker bonds (e.g., a triple bond will have a higher
wavenumber than a double or single bond, with an exception of hydrogen).
The term ‘group frequency’ refers to the absorption characteristics of reflected
light such as peak location (wavenumber range), type of vibration (vibrational
modes), and absorption intensity (strong, weak, sharp, variable) of an organic
functional group. Table 7.1 summarizes some absorption characteristics of key
functional groups. This information is very important in predicting the composition of the sample during analysis using MIR spectra.
In mid-infrared imaging, two common modes of data collection are transmission and
reflectance (Guo et al. 2004; Miller and Dumas 2006). The transmission mode is preferred during in vitro studies and require samples with low thickness variation to avoid
strong absorptions. For the in vivo studies, reflectance mode is preferred. In general, the
MIR spectroscopic imaging systems incorporate broadband incoherent thermal sources
with low optical spectral density and brightness. In addition, the reflected signal from
the sample is also weak for imaging. Researchers have been using laser system with
higher optical power, spectral density, and brightness to resolve this issue (Guo et al.
2004). The detectors used in MIR imaging can be infrared focal plane arrays (FPA), or
liquid-nitrogen-cooled mercury cadmium telluride (MCT) (Lewis et al. 1995; Huffman
et al. 2002; Guo et al. 2004; Miller and Dumas 2006). The concept and instrumentation
of Fourier transform infrared (FTIR) spectroscopic imaging has also been explained in
literature (Lewis et al. 1995; Huffman et al. 2002; Miller and Dumas 2006). The FTIR
technique has been explained further in MIR spectroscopy session.
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Table 7.1 Absorption characteristics of selective organic functional groups
Functional group
Range (cm−1)
Properties
Alkanes (–C–H)
2,850–3,000
1,350–1,470
720–725
3,020–3,100
1,900–2,000
1,630–1,680
675–995
3,300
2,100–2,250
3,580–3,650
3,200–3,550
1,330–1,430
970–1,250
650–770
3,400–3,500
3,300–3,400
1,550–1,650
1,000–1,250
660–900
2,690–2,840
1,720–1,740
1,710–1,720
1,690
1,675
1,745
1,780
1,350–1,360
1,400–1,450
1,100
2,500–3,300
1,700–1,725
1,395–1,440
1,210–1,320
2,240–2,260
2,550–2,600
700–900
500–540
Strong stretch
Medium bend
Weak rocking (bend)
Medium stretch (=C–H and =CH2)
Asymmetric stretch (C=C)
Symmetric variable stretch (C=C)
Medium/strong bend
Strong, sharp stretch (C–H)
Symmetric variable stretch (C≡C)
Variable, sharp stretch (free O–H)
Strong, broad stretch (O–H that is H–bonded)
Medium bend (O–H in-plane)
Strong stretch (C–O)
Weak, variable bend (O–H out-of-plane)
Weak stretch (N–H primary amines with 2 bands)
Weak stretch (N–H secondary amines)
Medium NH2 scissoring (primary amines)
Medium stretch (C–N)
Variable NH2 and NH wagging
Two bands, medium stretch
Strong stretch (C=O saturated aldehyde)
Strong stretch (C=O saturated ketone)
Strong stretch (aryl ketone)
Strong stretch (α, β-unsaturation)
Strong stretch (cyclopentanone)
Strong stretch (cyclobutanone)
Strong bend (α–CH3 bending)
Strong bend (α–CH2 bending)
Medium bend (C–C–C bending)
Strong, very broad stretch (O–H)
Strong stretch (C=O)
Medium bend (C–O–H bending)
Medium stretch (two peaks O–C)
Medium sharp, stretch
Weak, sharp stretch
Strong stretch
Weak stretch
Alkenes (=C–H)
Alkynes (–C≡C–)
Alcohol (–O–H)
Amines (–NH2)
Aldehydes (–CHO)
and Ketones (–C=O)
Carboxylic acid (–COOH)
Nitriles (C≡N)
Thiols (S–H)
Ester (S–OR)
Disulfide (S–S)
Mid-infrared Imaging Applications
The MIR imaging applications in food and agriculture have not been fully
explored. The two major applications of MIR imaging are in the fields of space
science and biology. The MIR/FTIR imaging have been used for studying neurotoxicity (Lewis et al. 1997), analysis of the biochemistry of plant and animal
S. Sankaran et al.
132
Infrared Source
Interferometer
Fixed Mirror
Beam
Splitter
Sample
Detector
Computer for data acquisition
and signal processing
Moving Mirror
Fig. 7.2 Schematic of FTIR spectroscopy
tissues (Wetzel and LeVine 1999), medical application such as detecting brain
tissues of Alzheimer’s patients, bone tissues such as arthritis patients, osteoporosis (Miller and Dumas 2006), and many more (Kastberger and Stachl 2003).
Similarly, it has also been applied in space science, especially to study galaxies
(Braatz et al. 1993; Soifer et al. 2000; Hainline et al. 2009).
Mid-infrared Spectroscopy
Mid-infrared spectroscopy generates a unique molecular fingerprint based on the
chemical composition of the sample. For these reasons, MIR spectroscopy has
been widely used in both food and agricultural applications. Commonly, MIR
spectroscopy utilizes some form of sample pre-treatment prior to analysis. There
are several different modes to acquire MIR spectra from a given sample (Wilson
and Tapp 1999). The first mode is the transmission mode, in which a single-beam
transmission mechanism is applied to acquire the MIR spectra from a sample. The
second mode is the reflectance mode. The reflectance can be diffuse reflectance or
attenuated total reflectance (ATR). The transmission mode can be used for analysis
of solids, liquids, or gases; while reflectance can only be used for solid and liquid
samples. Since the development of FTIR spectroscopy, the qualitative and quantitative analysis of samples using MIR spectroscopy has been widely expanded.
In FTIR spectroscopy, the infrared radiation from the source strikes the beam
splitter, where one half is directed to a fixed mirror and other half is directed to a
moving mirror (Fig. 7.2). The reflected radiation from both the mirrors is collected
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back by the beam splitter (with time delay for the radiation coming from the fixed
mirror) and is directed to the samples. These radiations from both the mirrors are
recombined. The difference in path lengths allows interference between the two
radiations. The interference signal is measured by the detector, and MIR spectrum
is generated by performing the Fourier transform of the measured signal. In MIR
spectroscopy, certain spectral regions are sensitive to moisture; therefore, researchers need to be aware of these regions, especially when analyzing biological samples.
The major benefits of MIR spectroscopy for analysis are high signal-to-noise ratio,
higher resolution and accuracy, and flexibility for multivariate data analysis.
Mid-infrared Spectroscopy Applications
Mid-infrared spectroscopy has widely been used for food quality and food safety
applications. Table 7.2 summarizes few representative studies on food quality
evaluation. The food materials can be in solid or liquid form. Solid food materials
include meat, grains, processed food, fruits, and butter while liquid food materials
include milk, oil, and juice products (VandeVoort 1992). Guillen and Cabo (1997)
reviewed the application of infrared spectroscopy for assessing fats and edible oils.
The sample preparation either involves dissolving the samples in ethyl ether or
cesium sulfide, or direct analysis. The analysis allows the possibility to monitor lipid
content, detection of adulterants (refined olive/walnut oil), degree of unsaturation
(iodine value), average molecular weight, solid fat index, and oxidation processes.
Kacurakova and Wilson (2001) reviewed the application of FTIR spectroscopy
for carbohydrate evaluation. They found spectral range of 840–890 cm−1 useful
in distinguishing monosaccharides such as glucose, galactose, and mannose. The
crystalline amylose and other oligosaccharides show peaks in spectral range of
600–1500 cm−1. Similarly, polysaccharides such as cellulose, xylan, pectin, and
starch have been evaluated using FTIR spectroscopy.
The properties of carboxymethyl starch, a starch derivative, which is used as
food thickener and stabilizer, can also be evaluated using FTIR spectroscopy. A
study has shown possibility of predicting the degree of substitution of carboxymethyl starch using a partial least square regression (PLSR) model with R2 of 0.9368
(Liu et al. 2012). The degree of substitution refers to the average number of carboxymethyl groups per anhydroglucose unit. The degree of substitution varied
from 0.06 to 0.28 in this study.
Another unique application of MIR spectroscopy is monitoring the quality
of medicine (Wu et al. 2008). The biomarkers α-pinene, methyl salicylate, and
eugenol could be detected and quantified using FTIR spectroscopy by identifying and analyzing specific spectral features associated with these biomarker molecules. The R2 values while predicting α-pinene, methyl salicylate, and eugenol
concentration during validation were 0.995, 0.987, and 0.999, respectively. MIR
spectroscopy has also been used for analyzing the degradation of by-products of
eating utensils which were made from biobased materials (Mulbry et al. 2012).
Adulteration
Type
Coconut oil
Virgin olive oil
Melamine
Coagulation property
Infant Formula
Milk
Thermo-Nicolet Nexus 670 FTIR
(4,000–650 cm−1)
Foss Electric FTIR interferometer with
MilkoScan FT120 (4,000–900 cm−1)
Jaco FTIR spectrometer (7,800–350 cm−1)
Fat content
Soluble solids, acidity
Blackcurrant
(Berries)
Milk
Milk Powder
Cherries
VERTEX 70 FTIR spectrometer
(4,000–700 cm−1)
Nicolet Magna 6,700 FTIR spectrophotometer
(4,000–400 cm−1)
TENSOR 27 mid-infrared spectrometer
(7,000−600 cm−1)
Nicolet 6700 FTIR spectrometer
(4,000–650 cm−1)
VERTEX 70 FTIR spectrometer
(4,000–700 cm−1)
TENSOR 27 instrument (4,000–550 cm−1)
Quality and
nutraceutical content
Anthocyanin
Fruits
Blueberries
Quality
Oil
Virgin olive oil
Bruker 55 equinox FTIR spectrometer
(4,000–500 cm−1)
Fatty acids
Pork
Sensor and sensor range
Adulteration (horse, soy) Perkin elmer 1600 FTIR (4,000–650 cm−1)
Parameter of interest
Food type
Meat
Beef
PLSR
Least square (LS) support vector
machine (SVM)
PLS
Statistical analysis (F-test, t-test,
and calibration curve)
PLSR
PLS regression (PLSR)
Principal component analysis
(PCA), LDA, and SIMCA
PLS-discriminant analysis (DA),
Linear discriminant analysis
(LDA), and SIMCA
PLS-DA
Soft independent modeling class
analogies (SIMCA) and partial
least square (PLS)
PLS and k-nearest neighbor (kNN)
Data analysis
Table 7.2 Some representative studies on food quality evaluation using mid-infrared spectroscopy
(continued)
Cecchinato et al. (2009)
Mauer et al. (2009)
Wu et al. (2007)
Camps et al. (2010)
Pappas et al. (2011)
Sinelli et al. (2008)
Sinelli et al. (2010)
Rohman et al. (2011)
Sinelli et al. (2007)
Flatten et al. (2005)
Meza-Marquez et al.
(2010)
References
134
S. Sankaran et al.
Cheese
Adulteration (margarine) FTS excalibur 3500GX FTIR spectrometer
(4,000–650 cm−1)
Sensory texture
ATI Mattson Infinity Series Fourier transform
spectrometer (4,000–640 cm−1)
Butter
Perkin elmer 160 FTIR (4,000–600 cm−1)
Type
Honey
Bruker FTIR spectrometer (4,000–650 cm−1)
Monit-IR spectrometer (4,000–800 cm−1)
Fungal contamination
Type
Others
Maize
Coffee
Red wines
Wines
Wines
Thermo electron FTIR spectrometer
(3,300–950 cm−1)
Quality
FT-MIR Nicolet Magna-IR 550 series II
(4,000–400 cm−1)
Type (organic/nonorganic) Thermo-Nicolet Bacchus/Multispec system
(4,000–400 cm−1)
Types
Equinox IFS 55 DTIR spectrometer
(4,000–700 cm−1)
Wine
Chinese rice wineSugars, acids
PLS
PCA
PLSR and factorial discriminant
analysis
PCA, discriminant analysis (DA), and
classification trees
PLSR
Cluster analysis and SIMCA
PLS, PCA, and LDA
PCA and PLSR
PLSR
Coagulation
Milk
Fagan et al. (2007)
Koca et al. (2010)
Bertelli et al. (2007)
Kos et al. (2002)
Downey et al. (1997)
Edelmann et al. (2001)
Cozzolino et al. (2009)
Cuadrado et al. (2005)
Shen et al. (2011)
Dal Zotto et al. (2008)
Parameter of interest
Protein
Food type
Milk
References
Etzion et al. (2004)
Mid- and Far-infrared Imaging
Table 7.2 (continued)
Sensor and sensor range
Data analysis
Vector 22 spectrophotometer (4,000–1000 cm−1)PLS, PCA, and artificial neural network (ANN)
MilkoScan FT120 (4,000–900 cm−1)
Statistical analysis (Pearson correlation, regression)
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This method was useful in evaluating the degradation of starch-polypropylene
­polymer (with starch degrading), but not polylactide degradation.
A more recent application of FTIR spectroscopy involves detection of foodborne pathogens and their properties. FTIR spectroscopy has been used to evaluate the effect of food processing techniques on microbial inactivation, membrane
properties of pathogens, microbial stress, changes in bacterial population and tolerance responses, and categorizing different microbial stains (Lamprell et al. 2006;
Alvarez-Ordonez et al. 2011). Most of these studies are on Escherichia coli and
Salmonella typhimurium. Pork meat spoilage studies using FTIR spectroscopy
indicated that 88 % classification accuracies can be achieved with an independent
dataset using PLS model (Papadopoulou et al. 2011). Similarly, FTIR spectroscopy can be used to quantify E. coli K-12 cells internalized in baby spinach, with
a 1,490–1,590 cm−1 fingerprint region (amide region) for the microbes. The peak
areas in the fingerprint region were correlated to E. coli concentrations with an R2
of 0.97 (Wang et al. 2010).
Chemometrics or spectral analysis is a critical part of FTIR spectroscopy. One
of the most commonly used data processing and analysis technique is the PLSR.
The PLSR involves reducing the dimensionality of the data and regression analyses. During PLSR, the PLS-extracted features (latent variables) use information
from both independent and response variable(s) such that the covariance between
the extracted features is maximized. This information is used to establish the relationship between the features and response(s). The major advantage of PLSR over
other methods is the potential to model multiple response variables along with
multiple independent variables during analysis. One of the major challenges is
that the interpretation of extracted features (such as latent variables) is not easy.
In addition to PLSR, there are other spectral analysis techniques that can be used
such as linear discriminant analysis (LDA), support vector machines (SVMs), and
soft independent modeling class analogies (SIMCA). Principal component analysis is another data dimensionality reduction technique that is often used. Some of
these chemometric techniques are described in Downey (1998).
The application of MIR spectroscopy in agriculture mainly involves using soil
analysis for evaluating macro- and micronutrients. MIR spectroscopy has been used
for determination of soil nitrate content (Ehsani et al. 2001; Sinfield et al. 2010);
however, it is very challenging in dry soil, so it is recommended that the samples
are prepared as a wet paste (Linker et al. 2004, 2006; Jahn et al. 2006). Similarly,
efforts have also been made for phosphorous detection using MIR spectroscopy.
Some of the micronutrients that have been tested include potassium, arsenic, copper, zinc, lead, and chromium (Sinfield et al. 2010; Dong et al. 2011). The soil carbon content has also been evaluated using visible–near-infrared (400–2,500 nm)
and MIR spectroscopy (2.5–25 µm) (McCarty et al. 2002; McDowell et al. 2012).
The total, organic, and inorganic carbon in the ranges of 0.98–104, 0.23–98, and
0–65 g/kg, respectively, were evaluated using both types of spectroscopy. MIR performed better than near-infrared spectroscopy, with an R2 of 0.97, 0.99, and 0.96,
while predicting total, inorganic, and organic (untreated) soil carbon, respectively
(McCarty et al. 2002). In addition to the nutrients, other soil parameters such as pH,
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137
Fig. 7.3 a The baseline corrected mid-infrared spectra showing the water (6 µm) and starch
peaks (9–10.5 µm), and b spectral features of processed starch (Sankaran et al. 2010)
lime requirement, cation exchange capacity have been evaluated using spectroscopic
methods (Reeves et al. 2001; Rossel et al. 2006).
In recent years, mid-infrared spectroscopy has been explored for plant stress
detection. Sankaran et al. (2010) utilized a portable MIR spectrometer to detect
huanglongbing (HLB), a citrus disease. The study reported the potential of MIR
spectroscopy for plant disease detection in early stages. The starch accumulation that occurs upon HLB infection could be identified using the spectroscopic
technique (Fig. 7.3). Starch accumulation can occur in HLB-infected leaves even
before the symptoms appear. Similar studies with an FTIR spectrometer also found
that the technique can detect starch accumulation in HLB-infected trees. The
starch peak occurs in the spectral region 1,111–8,439 nm (Hawkins et al. 2010).
Researchers have also used MIR spectroscopy to monitor biochemical or biomarkers that have significance in agriculture. Oleuropein, a biochemical compound that
has human health benefits, was monitored using MIR spectroscopy in olive leaves.
Data analyzed using PLSR resulted in R2 values of 0.91 and 0.74 during calibration and validation, respectively (Aouidi et al. 2012a). They also found that MIR
spectroscopy can be used for classifying the olive cultivars (Aouidi et al. 2012b).
Overall, mid-infrared spectroscopy has good potential for both food and agriculture applications. The major benefits include high sensitivity and specificity,
resistance to incident light changes, and the ability to establish a biochemical profile based on a spectral signature. One of the major limitations of this technique
is the requirement of some form of sample preparation, although there have been
studies with little to no sample preparation. In the future, innovative solutions may
develop to overcome this limitation.
Terahertz Imaging and Spectroscopy
The radiation between the infrared and microwave region of the electromagnetic spectrum with frequency ranging from 0.1 to 30 THz (100–30,000 µm)
is normally considered as terahertz (THz). Recent advances in photonics and
S. Sankaran et al.
138
Rapid
delay line
fs pulsed laser
Beam splitter
Emitter
Mirror
Sample
Detector
Mirror
Data acquisition
Fig. 7.4 Schematics of THz time-delay spectroscopy
related research fields have resulted in development of detectors that can work
in THz range. It has promoted the development of THz imaging and spectroscopy techniques capable of studying material properties at intermolecular levels
(Tonouchi 2007).
The key components of THz spectroscopy include a THz light source, an emitter, and a detector with the required optics arranged to obtain the time-domain
spectra in either transmission (Fig. 7.4) or reflectance mode. In the reflectance
mode, a set of parabolic mirrors between the emitter and detector needs to be used
to acquire the sample reflectance (Gowen et al. 2012). THz spectroscopy can also
be used as a THz imaging system with additional mechanical components (e.g.,
robot arm) to move the sample in the x- and y-directions of terahertz focal plane,
i.e., in raster scanning mode (Wang et al. 2011; Gowen et al. 2012) to acquire
amplitude at fixed time delay (BATOP Optoelectronics, Germany). The diameter
of the laser beam primarily governs the image resolution.
The systems are categorized as pulsed or continuous wave types depending on
the THz light source incorporated in it. In general, the THz time-domain spectroscopy technique measures electric-field amplitude (Tonouchi 2007) which can be
Fourier transformed to acquire the frequency-domain spectra.
THz imaging and spectroscopy techniques are being widely applied for explosives, weapons, and drug detection (Federici et al. 2005; Sinyukov et al. 2008;
Rahman 2011), pharmaceutical process quality (e.g., tablet coating characterization) monitoring (Shen 2011; Wu and Khan 2012), and inline process control and
quality inspection of polymer products (Jansen et al. 2010). In food processing,
THz sensing techniques have been used for nondestructive, real-time, and rapid
determination of food quality and bacterial contaminations. For example, THz
imaging and spectroscopy have been used to detect metallic and nonmetallic contaminants in chocolate bars (Jordens and Koch 2008). Researchers have explored
7
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139
spectroscopy technique more often compared to imaging technique. In foodstuff
evaluation, the THz spectroscopy has been explored to detect antibiotic residues
in packaged food (Redo-Sanchez et al. 2011), and low- and high-density metallic and nonmetallic foreign bodies in powdered instant noodles (Lee et al. 2012).
Features that make THz techniques a powerful and nondestructive sensing are as
follows: (1) minimal attenuation of THz radiation penetration by the food packaging materials (plastic, paper) that results in the high resolution images of food
with minimal interference of the packaging material, (2) nonionizing low photon
energy radiation is safe to be used with biological samples as it does not destroy
the sample itself, and (3) amplitude–phase-domain response of THz spectroscopy
provides vital material properties information compared with amplitude only
response of traditional spectroscopy techniques.
Applications of Terahertz Imaging and Spectroscopy
The THz technology has been explored in both food and agriculture. In regard to
food applications, THz technology is mainly used for food quality monitoring such
as detection of foreign objects, defects, and insect infestation. Some of the applications of this technology in the food industry are summarized in Table 7.3. THz
is a low-energy nonionizing radiation that is capable of penetrating nonconducting
matrices and is sensitive to moisture. This characteristics permit the inspection of
opaque objects inside food packages (Morita et al. 2007; Barnes et al. 2012), and
determination of moisture content in food products (Chau et al. 2004, 2005).
THz spectroscopy has been applied for detecting antibiotics in food and feed
matrices such as livestock feed, milk, and egg powder (Redo-Sanchez et al. 2011).
The study found that the absorbance peaks of sulfapyridine and doxycycline were
1.05 and 1.37 THz/dB/mg, respectively, in most food mixtures of antibiotics and
feed matrices (except doxycycline with feed). Similarly, THz spectroscopy has
been applied for characterizing oils (Gorenflo et al. 2006; Cunnell et al. 2009; Li
2010; Zhao et al. 2010; Jiang et al. 2011). For example, Zhao et al. (2010) developed THz time-domain spectroscopy-based rapid method to determine the purity
of standard vegetable oils. The THz spectroscopy has been explored to measure
saturated and unsaturated fatty acids such as palmitic, stearic, oleic, linoleic, and
linolenic acids (Jiang et al. 2011). The acids showed distinct patterns, while the
THz absorbance (second derivative) at 77 cm−1 was linearly correlated with oleic
acid concentrations between 18 and 855 mM. Naito et al. (2011) studied Fourier
transform THz spectrometer to evaluate fat (%), total solids (%), lactose (%), protein (%), and somatic cells (log/mL) contents in raw milk. The partial least square
model developed using the calibration data could predict the milk content with an
R2 of 0.72, 0.80, 0.38, 0.37, and 0.67, respectively.
In agriculture, THz spectroscopy has only been explored for very few applications. The applications include drought stress detection, quality evaluation of nuts,
grains, fruits, and other food products and crop yield estimation (Federici et al.
THz sensing approach
Shiraga et al. (2013)
Suhandy et al. (2011)
Kim et al. (2012)
Jiang et al. (2011)
Redo-Sanchez et al. (2011)
Zhao et al. (2010)
Li (2010)
Hua and Zhang (2010)
Parasoglou et al. (2010)
Morita et al. (2007)
Jordens and Koch (2008)
Jepsen et al. (2007)
Frequency range,
THz
References
THz spectrometer (transmis- 0.1–1.0
sion mode)
–
Seal defects in packaged food
Continuous THz wave system 0.6
Chocolate bar
Metallic and nonmetallic (stone, glass, or
THz spectrometer (transmis- 0.4–0.75
plastic particles) contaminations
sion mode) and imaging
Food wafers
Moisture content
THz spectrometer (transmis- 0.1–4.0
sion mode)
Food powders (sticky rice, sweet Pesticide detection
THz time-domain
0.5–1.6
potato, lotus root)
spectroscopy
Vegetable oil (sunflower, peanut, Dielectric properties, refractive indices and THz spectrometer (transmis- 0.2–1.5
soybean, rapeseed)
power absorption coefficient
sion mode)
Vegetable oils
Purity
THz time-domain
0.1–10
spectroscopy
Feed, milk, egg powder
11 antibiotic residues (e.g., sulfapyridine and Portable THz spectrometer 0.2–20
doxycycline)
(transmission mode)
Oily foods
Qualitative and quantitative analysis of fatty THz spectrometer
0.3–12
acids and their analogues
Vitamin C in aqueous solution Quantification in aqueous solution
FTIR-ATR Spectrometer
0.6–13.5
Food contaminants
Fungicides (carbendazim, chlorothalonil, and THz time-domain
0.2–3.5
isoprothiolane)
spectroscopy
Saccharides
Hydration state of saccharides (for sweetness THz spectrometer (ATR
0.2–3
and environmental stress tolerance)
mode)
Parameter of interest
Alcoholic beverages and liquors Sugar and alcohol content
Food type
Table 7.3 Food quality evaluation using terahertz imaging and spectroscopy techniques
140
S. Sankaran et al.
7
Mid- and Far-infrared Imaging
141
Table 7.4 Terahertz sensing for agricultural application
Application
Spectral
range (THz)
Results
References
Moisture in crushed
wheat grains
0.1–2
Chua et al. (2005)
Tomato quality evaluation
(defects and sugar content)
1
Quantitative analysis of water
content in spinach
0.189
Water content in coffee leaves
to monitor drought stress
0.3–1.8
Quality evaluation in pecans
(insect damage-living
manduca sexta and dry
pecan weevil)
0.2–20
Transmission linearly
proportional to
humidity (12–18 %)
100 % defect detection
(6 samples)
R2 = 0.91 for sugar
detection
Linear relationship
between THz
transmissionbased estimations
and measured water
content
Transmission at 300
GHz was inversely
proportional to
volumetric water
content
High water content
could be an issue
Ogawa et al. (2006)
Zhang et al. (2008)
Jordens et al. (2009)
Li et al. (2010)
2012). Table 7.4 summarizes some of the applications of THz technology in agriculture. Federici et al. (2009) attempted to predict the crop yield of berries such as
cherries, blueberries, and plums using the THz Gouy phase shift with THz imaging (Fig. 7.5). The THz imaging was advantageous due to its capability to image
fruit with high water content, and ability to penetrate through the thin canopy. The
number of clusters per vine could be predicted, which accounted for about 70 % of
the crop yield.
The THz technology has been used for evaluation of water content or drought
stress in plants (Ogawa et al. 2004; Zhang et al. 2008; Jördens et al. 2009;
Hadjiloucas et al. 1999, 2002). Hadjiloucas et al. (1999) used THz technique
(94 GHz) to quantify Catalpa bignonioides plant leaf water content. A linear relationship between the negative water potential (indicator of water stress) and transmittance was observed. In a water loss study (detached leaf dried for 50–200 h),
both Fatsia japonica and C. bignonioides leaves showed loss in water, with C. bignonioides leaves showing a rapid loss of water. Overall, the leaf thickness was the
limiting factor interfering the THz-based plant water content measurements.
THz technology is emerging as an advanced technique with potential food and
agricultural applications. It offers several benefits for rapid monitoring of plants
and food products that remain to be explored. However, high costs associated with
instrumentation, low data acquisition rates during imaging, high moisture content
142
S. Sankaran et al.
Fig. 7.5 a Visible image of
three grapes. The toothpick
in the picture is used to hold
the sample fixed during
the image acquisition. b
Corresponding THz image.
THz images are based on
average transmission between
0:15 and 0:2 THz of a grape c
and d a grape hidden behind
a grape leaf (Federici et al.
2009)
in the sample that absorbs THz radiation, effect of physical factors on measurements (particle size, leaf thickness, etc.), and THz wave transmission limits the
potential use of the technology (Yan et al. 2007; Gowen et al. 2012).
References
Alvarez-Ordonez A, Mouwen D, Lopez M, Prieto M (2011) Fourier Transform infrared spectroscopy as a tool to characterize molecular composition and stress response in foodborne pathogenic bacteria. J Microbiol Meth 84:369–378
Aouidi F, Dupuy N, Artaud J, Roussos S, Msallem M, Gaime I, Hamdi M (2012a) Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses. Ind Crops Prod 37:292–297
Aouidi F, Dupuy N, Artaud J, Roussos S, Msallem M, Perraud-Gaime I, Hamdi M (2012b)
Discrimination of five Tunisian cultivars by mid infrared spectroscopy combined with chemometric analyses of olive Olea europaea leaves. Food Chem 131:360–366
Barnes M, Dudbridge M, Duckett T (2012) Polarized light stress analysis and laser scatter imaging for non-contact inspection of heat seals in food trays. J Food Eng 112:183–190
Bertelli D, Plessi M, Sabatini A, Lolli M, Grillenzoni F (2007) Classification of Italian honeys by
mid-infrared diffuse reflectance spectroscopy (DRIFTS). Food Chem 101:1565–1570
Braatz JA, Wilson AS, Gezari DY, Varosi F, Beichman CA (1993) High-resolution mid-infrared imaging and astrometry of the nucleus of the Seyfert galaxy NGC 1068. Astrophys J
409:L5–L8
Camps C, Robic R, Bruneau M, Laurens F (2010) Rapid determination of soluble solids content
and acidity of Black currant (Ribes nigrum L.) juice by mid-infrared spectroscopy performed
in series. Lwt-Food Sci Technol 43:1164–1167
Cecchinato A, De Marchi M, Gallo L, Bittante G, Carnier P (2009) Mid-infrared spectroscopy
predictions as indicator traits in breeding programs for enhanced coagulation properties of
milk. J Dairy Sci 92:5304–5313
Chua HS, Upadhaya PC, Haigh AD, Obradovic J, Gibson AAP, Linfield EH (2004) Terahertz
time-domain spectroscopy of wheat grain, 12th international conference on Terahertz
Electronics. Conference digest of the 2004 joint 29th international conference on infrared
and millimeter waves, pp 399–400
7
Mid- and Far-infrared Imaging
143
Chua HS, Obradovic J, Haigh AD, Upadhya PC, Hirsch O, Crawley D, Gibson AAP, Gladden
LF, Linfield EH (2005) Terahertz time-domain spectroscopy of crushed wheat grain.
Microwave Symposium Digest IEEE MTT-S International, 2005
Cozzolino D, Holdstock M, Dambergs R, Cynkar W, Smith P (2009) Mid infrared spectroscopy
and multivariate analysis: a tool to discriminate between organic and non-organic wines
grown in Australia. Food Chem 116:761–765
Cuadrado M, de Castro M, Juan P, Gomez-Nieto M (2005) Comparison and joint use of near
infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination
of wine parameters. Talanta 66:218–224
Cunnell R, Luce T, Collins JHP, Rungsawang R, Freeman JR, Beere HE, Ritchie DA, Gladden
LF, Johns ML, Zeitler JA (2009) Quantification of emulsified water content in oil using a
terahertz quantum cascade laser, 34th international conference on infrared, millimeter, and
terahertz waves, 2009. IRMMW-THz pp 1–2
Dal Zotto R, De Marchi M, Cecchinato A, Penasa M, Cassandro M, Carnier P, Gallo L, Bittante
G (2008) Reproducibility and repeatability of measures of milk coagulation properties and
predictive ability of mid-infrared reflectance spectroscopy. J Dairy Sci 91:4103–4112
Dong Y, Yang S, Xu C, Li Y, Bai W, Fan Z, Wang Y, Li Q (2011) Determination of soil parameters
in apple-growing regions by near- and mid-infrared spectroscopy. Pedosphere 21:591–602
Downey G (1998) Food and food ingredient authentication by mid-infrared spectroscopy and
chemometrics. Trac-Trends Anal Chem 17:418–424
Downey G, Briandet R, Wilson R, Kemsley E (1997) Near- and mid-infrared spectroscopies in
food authentication: coffee varietal identification. J Agric Food Chem 45:4357–4361
Edelmann A, Diewok J, Schuster K, Lendl B (2001) Rapid method for the discrimination of red
wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts. J Agric Food
Chem 49:1139–1145
Ehsani M, Upadhyaya S, Fawcett W, Protsailo L, Slaughter D (2001) Feasibility of detecting soil
nitrate content using a mid-infrared technique. Trans ASAE 44:1931–1940
Etzion Y, Linker R, Cogan U, Shmulevich I (2004) Determination of protein concentration in raw
milk by mid-infrared Fourier transform infrared/attenuated total reflectance spectroscopy. J
Dairy Sci 87:2779–2788
Fagan C, Everard C, O’Donnell C, Downey G, Sheehan E, Delahunty C, O’Callaghan D (2007)
Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture
attributes of processed cheese. J Dairy Sci 90:1122–1132
Federici J (2012) Review of moisture and liquid detection and mapping using terahertz imaging.
J Infrared Millimeter Terahertz Waves 33:97–126
Federici J, Schulkin B, Huang F, Gary D, Barat R, Oliveira F, Zimdars D (2005) THz imaging
and sensing for security applications—explosives, weapons and drugs. Semicond Sci Technol
20:S266–S280
Federici J, Wample R, Rodriguez D, Mukherjee S (2009) Application of terahertz gouy phase
shift from curved surfaces for estimation of crop yield. Appl Opt 48:1382–1388
Flatten A, Bryhni E, Kohler A, Egelandsdal B, Isaksson T (2005) Determination of C22: 5 and
C22: 6 marine fatty acids in pork fat with Fourier transform mid-infrared spectroscopy. Meat
Sci 69:433–440
Gorenflo S, Tauer U, Hinkov I, Lambrecht A, Buchner R, Helm H (2006) Dielectric properties of
oil-water complexes using terahertz transmission spectroscopy. Chem Phys Lett 421:494–498
Gowen A, O’Sullivan C, O’Donnell C (2012) Terahertz time domain spectroscopy and imaging: emerging techniques for food process monitoring and quality control. Trends Food Sci
Technol 25:40–46
Guillen M, Cabo N (1997) Infrared spectroscopy in the study of edible oils and fats. J Sci Food
Agric 75:1–11
Guo B, Wang Y, Peng C, Zhang H, Luo G, Le H, Cho A (2004) Laser-based mid-infrared reflectance imaging of biological tissues. Opt Express 12(1):208–219
Hadjiloucas S, Karatzas L, Bowen J (1999) Measurements of leaf water content using terahertz
radiation. IEEE Trans Microw Theory Tech 47:142–149
144
S. Sankaran et al.
Hadjiloucas S, Galvao R, Bowen J (2002) Analysis of spectroscopic measurements of leaf water
content at terahertz frequencies using linear transforms. J Opt Soc Am-Opt Image Sci Vis
19:2495–2509
Hainline LJ, Blain AW, Smail I, Frayer DT, Chapman SC, Ivison RJ, Alexander DM (2009) A
mid-infrared imaging survey of submillimeter-selected galaxies with the spitzer space telescope. Astrophys J 699(2):1610
Hawkins S, Park B, Poole G, Gottwald T, Windham W, Albano J, Lawrence K (2010)
Comparison of FTIR spectra between huanglongbing (citrus greening) and other citrus maladies. J Agric Food Chem 58:6007–6010
Hua Y, Zhang H (2010) Qualitative and quantitative detection of pesticides with terahertz timedomain spectroscopy. IEEE Trans Microw Theory Tech 58:2064–2070
Huffman SW, Bhargava R, Levin IW (2002) Generalized implementation of rapid-scan Fourier
transform infrared spectroscopic imaging. Appl Spectrosc 56(8):965–969
Jahn B, Linker R, Upadhyaya S, Shaviv A, Slaughter D, Shmulevich I (2006) Mid-infrared spectroscopic determination of soil nitrate content. Biosyst Eng 94:505–515
Jansen C, Wietzke S, Peters O, Scheller M, Vieweg N, Salhi M, Krumbholz N, Jordens C, Hochrein
T, Koch M (2010) Terahertz imaging: applications and perspectives. Appl Opt 49:E48–E57
Jepsen P, Moller U, Merbold H (2007) Investigation of aqueous alcohol and sugar solutions with
reflection terahertz time-domain spectroscopy. Opt Express 15:14717–14737
Jiang F, Ikeda I, Ogawa Y, Endo Y (2011) Terahertz absorption spectra of fatty acids and their
analogues. J Oleo Sci 60:339–343
Jördens C, Koch M (2008) Detection of foreign bodies in chocolate with pulsed terahertz spectroscopy. Opt Eng 47
Jördens C, Scheller M, Breitenstein B, Selmar D, Koch M (2009) Evaluation of leaf water status
by means of permittivity at terahertz frequencies. J Biol Phys 35:255–264
Kacurakova M, Wilson R (2001) Developments in mid-infrared FT-IR spectroscopy of selected
carbohydrates. Carbohydr Polym 44:291–303
Kastberger G, Stachl R (2003) Infrared imaging technology and biological applications. Behav
Res Methods, Instr, Comput 35(3):429–439
Kim G, Lee SD, Moon JH, Kim KB, Lee DK (2012) Terahertz technology for the detection of
food contaminants, 37th international conference on infrared, millimeter, and terahertz waves
(IRMMW-THz), 2012, pp 1–2
Koca N, Kocaoglu-Vurma N, Harper W, Rodriguez-Saona L (2010) Application of temperaturecontrolled attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy for rapid estimation of butter adulteration. Food Chem 121:778–782
Kos G, Lohninger H, Krska R (2002) Fourier transform mid-infrared spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the detection of fusarium fungi on maize. Vib
Spectrosc 29:115–119
Lamprell H, Mazerolles G, Kodjo A, Chamba J, Noel Y, Beuvier E (2006) Discrimination of
Staphylococcus aureus strains from different species of Staphylococcus using Fourier transform infrared (FTIR) spectroscopy. Int J Food Microbiol 108:125–129
Lee Y, Choi S, Han S, Woo D, Chun H (2012) Detection of foreign bodies in foods using continuous wave terahertz imaging. J Food Prot 75:179–183
Lewis EN, Treado PJ, Reeder RC, Story GM, Dowrey AE, Marcott C, Levin IW (1995) Fourier
transform spectroscopic imaging using an infrared focal-plane array detector. Anal Chem
67(19):3377–3381
Lewis E, Kidder LH, Levin IW, Kalasinsky VF, Hanig JP, Lester DS (1997) Applications
of Fourier transform infrared imaging microscopy in neurotoxicity. Ann N Y Acad Sci
820(1):234–247
Li J (2010) Optical Parameters of Vegetable Oil Studied by terahertz time-domain Spectroscopy.
Appl Spectrosc 64:231–234
Li B, Cao W, Mathanker S, Zhang WL, Wang N (2010) Preliminary study on quality evaluation
of pecans with terahertz time-domain spectroscopy In: Proceedings of SPIE 7854, Infrared,
Millimeter Wave, and Terahertz Technologies, 2010
7
Mid- and Far-infrared Imaging
145
Linker R, Kenny A, Shaviv A, Singher L, Shmulevich I (2004) Fourier Transform Infraredattenuated total reflection nitrate determination of soil pastes using principal component
regression, partial least squares, and cross-correlation. Appl Spectrosc 58:516–520
Linker R, Weiner M, Shmulevich I, Shaviv A (2006) Nitrate determination in soil pastes using
attenuated total reflectance mid-infrared spectroscopy: improved accuracy via soil identification. Biosyst Eng 94:111–118
Liu J, Chen J, Dong N, Ming J, Zhao G (2012) Determination of degree of substitution of carboxymethyl starch by Fourier transform mid-infrared spectroscopy coupled with partial least
squares. Food Chem 132:2224–2230
Mauer L, Chernyshova A, Hiatt A, Deering A, Davis R (2009) Melamine detection in infant formula powder using near- and mid-infrared spectroscopy. J Agric Food Chem 57:3974–3980
McCarty G, Reeves J, Reeves V, Follett R, Kimble J (2002) Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci Soc Am J 66:640–646
McDowell M, Bruland G, Deenik J, Grunwald S, Knox N (2012) Soil total carbon analysis in
Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy.
Geoderma 189:312–320
Meza-Marquez O, Gallardo-Velazquez T, Osorio-Revilla G (2010) Application of mid-infrared
spectroscopy with multivariate analysis and soft independent modeling of class analogies
(SIMCA) for the detection of adulterants in minced beef. Meat Sci 86:511–519
Miller LM, Dumas P (2006) Chemical imaging of biological tissue with synchrotron infrared
light. Biochim et Biophys Acta (BBA)-Biomembranes 1758(7):846–857
Morita Y, Dobroiu A, Otani C, Kawase K (2007) Real-time terahertz diagnostics for detecting microleak defects in the seals of flexible plastic packaging. J Adv Mech Des Sys Manuf 1:338–345
Mulbry W, Reeves J, Millner P (2012) Use of mid- and near-infrared spectroscopy to track degradation of bio-based eating utensils during composting. Bioresour Technol 109:93–97
Naito H, Ogawa Y, Shiraga K, Kondo N, Hirai T, Osaka I, Kubota A (2011) Inspection of milk
components by terahertz attenuated total reflectance (THz-ATR) spectrometer equipped temperature controller. IEEE/SICE international symposium on system integration, pp 192–196
Ogawa Y, Hayashi S, Kondo N, Ninomiya K, Otani C, Kawase K (2006) Feasibility on the quality evaluation of agricultural products with terahertz electromagnetic wave. 2006 ASABE
Annual international meeting, pp 1–12
Ogawa Y, Kawase K, Mizuno M, Yamashita M, Otani C (2004) Nondestructive and real-time
measurement of moisture in plant. IEEJ Trans Electron, Inf Syst 124:1672–1677
Papadopoulou O, Panagou E, Tassou C, Nychas G (2011) Contribution of Fourier transform
infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat
spoilage. Food Res Int 44:3264–3271
Pappas C, Takidelli C, Tsantili E, Tarantilis P, Polissiou M (2011) Quantitative determination of
anthocyanins in three sweet cherry varieties using diffuse reflectance infrared Fourier transform spectroscopy. J Food Compos Anal 24:17–21
Parasoglou P, Parrott EPJ, Zeitler JA, Rasburn J, Powell H, Gladden LF, Johns ML (2010)
Quantitative water content measurements in food wafers using terahertz radiation. Terahertz
Sci Technol 3:172–182
Rahman A (2011) Dendrimer based terahertz time-domain spectroscopy and applications in
molecular characterization. J Mol Struct 1006:59–65
Redo-Sanchez A, Salvatella G, Galceran R, Roldos E, Garcia-Reguero J, Castellari M, Tejada J
(2011) Assessment of terahertz spectroscopy to detect antibiotic residues in food and feed
matrices. Analyst 136:1733–1738
Reeves J, McCarty G, Reeves V (2001) Mid-infrared diffuse reflectance spectroscopy for the
quantitative analysis of agricultural soils. J Agric Food Chem 49:766–772
Rohman A, Man Y (2011) The use of Fourier transform mid infrared (FT-MIR) spectroscopy for
detection and quantification of adulteration in virgin coconut oil. Food Chem 129:583–588
Rossel R, Walvoort D, McBratney A, Janik L, Skjemstad J (2006) Visible, near infrared, mid
infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various
soil properties. Geoderma 131:59–75
146
S. Sankaran et al.
Sankaran S, Ehsani R, Etxeberria E (2010) Mid-infrared spectroscopy for detection of
Huanglongbing (greening) in citrus leaves. Talanta 83:574–581
Shen Y (2011) Terahertz pulsed spectroscopy and imaging for pharmaceutical applications: A
review. Int J Pharm 417:48–60
Shen F, Ying Y, Li B, Zheng Y, Hu J (2011) Prediction of sugars and acids in Chinese rice wine
by mid-infrared spectroscopy. Food Res Int 44:1521–1527
Shiraga K, Ogawa Y, Kondo N, Irisawa A, Imamura M (2013) Evaluation of the hydration state of saccharides using terahertz time-domain attenuated total reflection spectroscopy. Food Chem 140:2
Sinelli N, Casale M, Di Egidio V, Oliveri P, Bassi D, Tura D, Casiraghi E (2010) Varietal discrimination of extra virgin olive oils by near and mid infrared spectroscopy. Food Res Int
43:2126–2131
Sinelli N, Cosio M, Gigliotti C, Casiraghi E (2007) Preliminary study on application of mid
infrared spectroscopy for the evaluation of the virgin olive oil “freshness”. Anal Chim Acta
598:128–134
Sinelli N, Spinardi A, Di Egidio V, Mignani I, Casiraghi E (2008) Evaluation of quality and
nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared
spectroscopy. Postharvest Biol Technol 50:31–36
Sinfield J, Fagerman D, Colic O (2010) Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils. Comput Electron Agric 70:1–18
Sinyukov A, Zorych I, Michalopoulou Z, Gary D, Barat R, Federici J (2008) Detection of explosives by terahertz synthetic aperture imaging—focusing and spectral classification. CR Phys
9:248–261
Soifer BT, Neugebauer G, Matthews K, Egami E, Becklin EE, Weinberger AJ, Condon JJ (2000)
High resolution mid-infrared imaging of ultraluminous infrared galaxies. The Astron J 119(2):509
Suhandy D, Yulia M, Ogawa Y, Kondo N (2011) Prediction of vitamin C using FTIR-ATR terahertz spectroscopy combined with interval partial least squares (iPLS) regression, 2011
IEEE/SICE international symposium on system integration (SII), pp 202–206
Tonouchi M (2007) Cutting-edge terahertz technology. Nat Photonics 1:97–105
Vandevoort F (1992) Fourier-transform infrared-spectroscopy applied to food analysis. Food Res
Int 25:397–403
Wang J, Kim K, Kim S, Kim Y, Li Q, Jun S (2010) Simple quantitative analysis of Escherichia
coli K-12 internalized in baby spinach using Fourier Transform Infrared spectroscopy. Int J
Food Microbiol 144:147–151
Wang Y, Zhao Z, Chen Z, Zhang L, Kang K, Deng J (2011) Continuous-wave terahertz phase
imaging using a far-infrared laser interferometer. Appl Opt 50:6452–6460
Wetzel DL, LeVine SM (1999) Imaging molecular chemistry with infrared microscopy. Science
285(5431):1224–1225
Wilson R, Tapp H (1999) Mid-infrared spectroscopy for food analysis: recent new applications and
relevant developments in sample presentation methods. Trac-Trends Anal Chem 18:85–93
Wu D, Feng S, He Y (2007) Infrared spectroscopy technique for the nondestructive measurement
of fat content in milk powder. J Dairy Sci 90:3613–3619
Wu H, Khan M (2012) THz spectroscopy: an emerging technology for pharmaceutical development and pharmaceutical process analytical technology (PAT) applications. J Mol Struct
1020:112–120
Wu Y, Sun S, Zhou Q, Leung H (2008) Fourier transform mid-infrared (MIR) and near-infrared
(NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua
Oil. J Pharm Biomed Anal 46:498–504
Yan Z, Zhang H, Ying Y (2007) Research progress of terahertz wave technology in quality measurement of food and agricultural products. Spectrosc Spectral Anal 27:2228–2234
Zhang H, Mitobe K, Yoshimura N (2008) Application of terahertz imaging to water content
measurement. Jpn J Appl Phys 47:8065–8070
Zhao Z, Chen Z, Wang Y, Feng B, Zhang L, Liu Z, Liang Y (2010) Method and apparatus
for assessing purity of vegetable oils by means of terahertz time-domain spectroscopy,
Washington, US. Patent 7:651–769
Chapter 8
Thermal Infrared Imaging
M. Teena and A. Manickavasagan
Introduction
Thermal imaging is a non-contact method in which the radiation pattern of an object
is converted into a visible image called thermal image or thermogram. All the objects
at temperature above absolute zero (−273 °C) emit infrared radiation. The infrared
band with wavelength from 3 to 14 µm is called thermal infrared region. This is used
in imaging applications that uses heat signatures. Thermal imaging maps the surface
temperature of any object with high thermal and spatial resolutions.
Thermal imaging may be broadly classified into two types, namely active thermography and passive thermography. In active thermography, the object is heated
or cooled before imaging, whereas in passive method, the object is imaged at natural state or steady state without heating or cooling prior to imaging (Gowen et al.
2010).
Principle of Thermal Imaging
The emissivity, absorptivity, transmissivity, and reflectivity properties of infrared radiation differ among various types of materials. Kirchhoff’s law derives the relationship
between absorptivity (α), reflectivity (ρ), and transmissivity (τ) of an object Eq. (8.1):
α+ρ+τ =1
(8.1)
M. Teena · A. Manickavasagan (*)
Department of Soils, Water and Agricultural Engineering,
College of Agricultural and Marine Sciences, Sultan Qaboos University,
P O Box 34, Al Khoud PC 123, Sultanate of Oman
e-mail: manick@squ.edu.om
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_8, © Springer-Verlag Berlin Heidelberg 2014
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M. Teena and A. Manickavasagan
148
At thermal equilibrium state, the absorption of an object is equal to its emission.
In thermographic applications, for opaque objects (τ = 0), the law can be further
simplified to Eq. 8.2:
α+ρ =1
(8.2)
ε+ρ =1
(8.3)
or
where ε is emissivity.
The detectors in thermal camera receive the total infrared radiation emitted
from the surface of the objects. The total amount of radiation (E) emitted by an
object per unit area is directly related to the emissivity of the object and its temperature (Stefan–Boltzmann law) as explained in Eq. 8.4:
E = σ ε T4
(8.4)
where
E
σ
ε
T
Total amount of radiation emitted by an object per unit area (W/m2)
Stefan–Boltzmann’s constant = 5.67 × 10−8 W/m2 K4
Emissivity of the object and
Temperature of the object (K)
Therefore, the surface temperature of the object is basically estimated based on the
total amount of the infrared energy emitted by it. Atmospheric conditions such as
temperature, wind velocity, and relative humidity (RH) may influence the signal
acquired by the thermal camera.
Emissivity
The emissivity of the surface of the object may be defined as the ratio of the target surface radiance to that of blackbody at the same temperature, viewed from the
same angle, and over the same spectral interval. The emissivity of the object may
vary with wavelength, the object’s shape, surface quality, and viewing angle. In general, the emissivity of the material will be maximum when observed perpendicular
to its surface (Infrared training center 2002). Figure 8.1 shows the emissivity of
several common objects.
Infrared Imaging Classification
Thermal infrared region may be roughly classified into four categories. The first
region is short wavelength infrared imaging band (SWIR) which approximately covers 1.1–2.5 µm. The second band is mid-wavelength infrared imaging (MWIR) in
8 Thermal Infrared Imaging
149
Fig. 8.1 Emissivity
of different materials
(Reproduced from Holst 2000
with permission from SPIE)
the range 2.5–7.0 µm. The third region is long-wavelength infrared imaging (LWIR)
which approximately covers 7.0–15.0 µm. The fourth imaging region in infrared
is called very-long-wave infrared (VLWIR) whose spectral response extends past
15 µm. The MWIR and LWIR regions are called the first and second thermal imaging bands, respectively (Holst 2000). These two bands have higher transmission in
the atmosphere. The short wave cameras are sensitive to solar reflection and extra
care should be given while using for outdoor application (Infrared training center
2002). At higher aerosol concentration in the atmosphere, the performance of MWIR
is affected more than LWIR region. But water vapor affects LWIR more than SWIR
region. The MWIR has better atmospheric path lengths than LWIR region (Holst
2000). Therefore, the type of camera must be selected correctly based on application.
Atmospheric Factors
In general, the atmospheric conditions such as humidity, water vapor, wind, and
aerosol affect the transmission of infrared radiation from the target object to the
thermal imaging system. High humidity in the atmosphere reduces the transmittance. Manickavasagan et al. (2006b) reported that the wind velocity of even
1–2 m/s affected the performance of thermal imaging system. Similarly, the path
distance also plays an inverse role in atmospheric transmittance of infrared radiation. Figure 8.2 explains the atmospheric transmittance at different path lengths.
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M. Teena and A. Manickavasagan
Fig. 8.2 Atmospheric transmittance of infrared radiation at various path lengths: a 10 m;
b 0 m; c 100 m; d 2 km; e 5 km; f 10 km (Reproduced from Holst 2000 with permission from
SPIE)
Detectors and Lenses
The detectors used in thermal cameras may be broadly classified into three categories: classical semi-conductors, novel semi-conductors, and thermal detectors
(Holst 2000). The classical semi-conductor includes photoconductive and photovoltaic detector types. Schottky barrier photodiode (SBD) and bandgap engineered photodetectors are the two types in the novel semi-conductor detectors.
Similarly, bolometer and pyroelectric are the two available types in thermal detectors (Holst 2000).
The lenses for thermal cameras are usually made of silicon (Si) or germanium
(Ge) materials. In general, Si is used for MWIR cameras and Ge is used in LWIR
cameras. Both materials have good mechanical properties (non-hygroscopic and
do not break easily). While making proper design, infrared camera lenses can
transmit close to 100 % of incident radiation (FLIR 2012).
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151
Thermal Imaging Cameras
With advancement in electronics and instrumentation technology, there are several
thermal camera models available in the market at wide price ranges. Table 8.1 explains
various models of research thermal cameras manufactured by FLIR Company.
Applications in Food and Agriculture
Food industries are constantly investigating innovative techniques for improving
food quality and safety. Many applications using computer vision technology have
been developed in food and agricultural area for precision farming, post-harvest
product quality and safety detection, grading and sorting, and process automation.
Besides visible imaging, machine vision systems are also able to inspect objects in
invisible spectrums such as ultraviolet, near-infrared, and thermal infrared regions
(Meola and Carlomagno 2004).
Thermal imaging measures and maps the entire surface temperature of an object
with high temporal and spatial resolutions when compared to other single-point
measuring instruments such as thermocouples and thermometers. Thermal imaging is a promising tool for determining pre-harvest and post-harvest quality indices such as crop maturity, diseases or defects, stress states, composition, functional
properties, infestations, and contamination by foreign particles. Thermal imaging
is an emerging, non-invasive, and non-destructive analytical technique ideal for
food industries. This technique can be used in fields related to temperature variations and evaluation of processes or products. Potential applications of thermal
imaging in food and agriculture includes estimation of crop water stress, irrigation
scheduling, disease and pathogen detection in plants, predicting fruit yield, maturity evaluation, post-harvest bruise detection in fruits, detection of foreign bodies
in food products, and temperature distribution properties while cooking.
Pre-harvest Operation
Water Status and Stress
During irrigation in the farm, some water is stored in the soil to be utilized by
crops, whereas the remaining is lost by evaporation, runoff, or seepage. Sensible
irrigation scheduling should minimize water losses, which in turn maximize the
irrigation efficiency and yield by reducing energy and water usage. Alternatively,
excess irrigation can result in excess soil moisture which may lead to crop diseases, nutrient leaching, and reduced pesticide effectiveness. Hence, irrigation
scheduling requires major understanding of the soil water status and crop stress.
InSb
Microbolometer
InGaAs
LWIR
MWIR
LWIR
SWIR
A655sc
A6700sc
T650sc
A2600sc
Microbolometer
Microbolometer
LWIR
T450sc
Sensor type
Microbolometer
Wave band
LWIR
Image
A325sc
Model
640 × 512
640 × 480
640 × 512
640 × 480
320 × 240
320 × 240
Pixel resolution
Table 8.1 Research thermal camera models and their specifications (FLIR 2012)
0.9–1.7
7.5–13.0
3.0–5.0
7.5–14.0
7.5–13.0
7.5–13.0
−40–2,000
−20–350
−40–150, or 100–650
−40–1,500
−20–120, or 0–350
Spectral range (μm) Standard camera
­calibration range (°C)
30
30
60
50
30
60
(continued)
Digital full
frame rate (Hz)
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M. Teena and A. Manickavasagan
MCT
InSb
InSb
InSb
InSb
MWIR
MWIR
MWIR
MWIR
GF335
MWIR
SC6000
series
SC8000
series
RS6700
series
Sensor type
LWIR
Wave band
InSb,
Image
MWIR,
SC7000
series
Model
Table 8.1 (continued)
640 × 512
1,344 × 784
1,024 × 1,024
640 × 512
320 × 240
320 × 256 or
640 × 512
640 × 512,
Pixel resolution
3.0–5.0
1.0–5.0, or 3.0–5.0
1.0–5.0, 1.–5.0, or
3.0–5.0
3.0–5.0
−20–500
−20–350 or −20–500
−20–300
7.7–9.3 or 7.85–9.5 5–150 (MCT)
Spectral range (μm) Standard camera
­calibration range (°C)
3.0–5.0 or 1.5–5.1, 5–300 (InSb)
0.0015–126
programmable
132
0.0015–126
programmable
60
115–235
Digital full
frame rate (Hz)
5–100 programmable
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M. Teena and A. Manickavasagan
Fig. 8.3 Artificially combined water status map derived from LWP maps calculated for images
from August 2003: a based on CWSI, b based on leaf temperature (Reproduced from Cohen
et al. 2005 with permission from Oxford University Press)
Conventional methods of estimating soil water content in an agricultural field
such as soil sampling and time domain reflectometry are normally carried out
by spot analysis (Sugiura et al. 2007). Image-based remote sensing could solve
the problem of poor spatial resolution of the above techniques, thereby effectively monitoring water status across the field. Sugiura et al. (2007) developed
a thermal imaging system by taking images of a paddy field from a low-altitude
unmanned helicopter at 10 am and 3 pm on the same day. The determination coefficient between water content and temperature difference was 0.42 after correcting
the transmissivity error. Thermal imagery was found to be efficient in estimating
on-field soil water status. In another study, the potential of a radiometric infrared video camera for an in-field estimation of the water status of cotton crop was
determined by Cohen et al. (2005). The leaf water potential (LWP) and the leaf
surface temperature were measured from the images (Fig. 8.3). A stable relationship between crop water stress index (CWSI) and LWP was found. The classified
LWP maps showed that there was spatial variability among treatments involving
sunlit and shaded leaves. Midday was found to be the optimum time to determine
crop water status (Alchanatis et al. 2010).
Achieving high-quality produce depends on the ability to maintain optimum levels
of water stress in the growing crop. Moller et al. (2007) investigated the use of thermal imaging for monitoring water stress on wine grape (Vitis vinifera cv.Merlot) in a
vineyard with three different irrigation treatments such as mild, moderate, and severe
stress. Thermal images of the crop were taken on four days at midday by an uncooled
infrared thermal camera mounted on a truck–crane 15 m above the canopy. An artificial wet surface was used to estimate the reference wet temperature (Twet). Crop
parameters such as stem water potential (Ψstem), leaf conductance (gL), and leaf area
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index were monitored, and it was found that CWSI was highly correlated with gL
(R2 = 0.91) and moderately correlated with Ψstem. Grant et al. (2007) suggested that
measuring the average temperatures of areas of irrigated and non-irrigated canopies
containing several leaves may be more useful than measuring individual leaves.
The stomatal closure can be caused by multiple reasons such as drought, flooding, salinity stress, fungal infection, or pollutants. Researchers have suggested that
a multi-sensor imaging such as combined thermal and reflectance imaging system
is required to diagnose and monitoring crop stress effectively (Chaerle et al. 2001;
Jones and Schofield 2008; Meron et al. 2013).
Water Loss
A one-layer resistance model combined with infrared thermometry was used to estimate evaporation rate from pastures (Kalma and Jupp 1990). A significant r­elative
error was observed in dry conditions and at low net radiation. The ­differences
between computed and observed surface temperatures were probably caused by
errors in measuring the sensible heat flux, the surface temperature, and the aerodynamic resistance. Leaf transpiration rate and stomatal resistance were measured
using infrared radiometer and correlated with steady state porometer measurements
and obtained a linear relation (r = 0.79 and 0.93) among them (Inoue et al. 1990).
Likewise, the sensitivity of leaf temperature to evaporation rate and stomatal conductance could be estimated by thermal imaging (Jones 1999, 2004). It was found
that stomatal conductance of crops under water deficit conditions was lowered by
increased water-use efficiency (Jones et al. 2002; Grant et al. 2012).
Ice Nucleation
The process of ice nucleation leads to frost hardiness in a frozen plant tissues. A valid
assessment of plant frost hardiness is required to prevent cataclysmic damage. The
common method used to detect ice formation in plant tissues is by multiple thermocouples and examining the exotherm. This method was complicated and unreliable for
routine evaluation. Hence, there was a need for a faster, non-invasive, and consistent
technique to detect ice nucleation. An infrared video imaging system placed in a freezing chamber was used to record the ice nucleation events in two crop species, potato
tubers (variety Russet Burbank) and cauliflower curd (class 1) (Fuller and Wisniewski
1998). It was reported that supercooling of potato plants could be done by moderately
lowering temperatures from −6 to −8 °C without causing any physiological damage.
This study also confirmed the discrete freezing ability of cauliflower florets within
an intact curd (Fig. 8.4). Similarly, individual leaves of barley (Hordeum vulgare,
Hordeum murinum, and Holcus lanatus) were also able to freeze separately both in
the laboratory and field study using the above technique (Pearce and Fuller 2001).
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M. Teena and A. Manickavasagan
Fig. 8.4 Video stills of cauliflower curd showing independent nucleation events in florets. a
Supercooled curd with differential temperatures across surface time 0 s. b 1st nucleation event
arrowed time 0 min 22 s. c Progression of freezing within the floret time 0 min 35 s. d First fl
­ oret
fully frozen but freezing confined and unable to spread further time 1 min 02 s. e Curd after
three independent nucleation events time 9 min 49 s. f Curd after nine independent nucleation
events but still showing some unfrozen florets time 49 min 32 s (Reproduced from Fuller and
Wisniewski 1998 with permission from Elsevier Ltd.)
Pathogen Interaction
Crops can be affected by infections caused by fungi, bacteria, viruses, and nematodes. Fungi are mainly responsible for a range of serious plant diseases such as
blight, gray mold, powdery mildew, and downy mildew (Hellebrand et al. 2006).
Infected crops can suffer from losses in yield and quality and also may result in
the production of toxic substances such as mycotoxins. Therefore, it is essential to
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Fig. 8.5 Thermal images of healthy (a) and sprout-damaged (b) barley kernels (Reproduced
from Vadivambal et al. 2011 with permission from International Commission of Agricultural and
Biosystems Engineering)
identify the occurrence of diseases in the plant materials at an early stage to take
corrective measures before the spread.
Natural resistance of plants toward tobacco mosaic virus (TMV) induces the
production of salicylic acid (SA). When SA was applied to resistant tobacco
leaves, the leaf temperature increased which could be measured by thermal imaging (Chaerle et al. 1999). Direct infection by artificially inoculating tobacco leaves
with TMV inoculums also produced similar temperature variations. Subsequent
cell death of TMV-infected leaves was illustrated by a complex lesion phenotype
which resulted in changes in transpiration (Linke et al. 2000; Chaerle et al. 2001).
Lindenthal et al. (2005) analyzed infected and non-infected cucumber leaves by
Pseudoperonospora cubensis (causing downy mildew) by a combined application
of digital infrared thermography with measurements of gas exchange. A negative
correlation of leaf transpiration rate to leaf temperature was observed (r = −0.76).
Oerke at el. (2006) measured the temperature difference within a leaf by thermography to evaluate the spatial heterogeneity of leaf temperature under controlled
conditions. Fungal infections (powdery mildew and stripe rust) in wheat plants
were detected by thermal imaging under laboratory conditions by Hellebrand
et al. (2006). However, field applications of thermal imaging did not produce similar results due to natural temperature variations within the crop canopy and the
low-resolution imaging system.
Defects Detection
Pre-harvest sprouting of grains is a major problem that affects the end-product quality. The techniques to determine sprout damage such as falling number,
stirring number, and rapid visco analyzer are time consuming and destructive.
Thermal imaging has the potential to detect the changes in grain surface temperature distribution depending on its heat emission. An infrared thermal camera was
used to determine the sprout damage in barley and wheat (Vadivambal et al. 2010,
2011) (Fig. 8.5). The results analyzed using linear discriminant analysis (LDA),
quadratic discriminant analysis (QDA), and artificial neural network (ANN)
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classifiers showed higher classification accuracies were achieved for wheat to
distinguish between sprouted and healthy kernels than for barley. ANN and LDA
yielded 98–99 % accuracies to determine healthy and sprouted wheat kernels.
Another study demonstrated the capability of thermal imaging for the detection
of Huanglongbing (HLB) disease (greening) in citrus trees by measuring the canopy
temperature changes (Sankaran et al. 2013). Thermal infrared spectral reflectance data
were collected from individual healthy and HLB-infected trees in the orchard. Thirteen
thermal bands in the infrared region showed maximum class separability between
healthy and HLB-infected groups using various classifiers such as LDA, QDA, bagged
decision tree (BDT), and support vector machine (SVM). The SVM classifier yielded
an overall classification accuracy of 87 % with minimum false negatives.
Harvesting, Post-harvest Handling, and Storage
The appropriate stage of maturity of a fresh produce at the time of harvest is crucial to maintain its quality during storage and marketing. Skin color, shape, size,
aroma, and firmness are some of the quality parameters used for determining the
maturity levels. Detecting ripe fruits in a large orchard requires more skilled workers and time. Hence, automatic detection of mature fruits and vegetables in the
farm is highly beneficial for mechanical harvesting. Similarly to ensure the quality
and safety of food, good quality assurance practices must be used throughout the
supply chain such as on-farm, post-harvest handling, processing, packaging, storage, and preparation prior to consumption.
Fruits Detection
Thermal imaging was tested for estimating the number of Golden Delicious apple
fruits and measuring their diameter within the orchard. A total of 120 images
of twenty apple trees were captured in the late afternoon to achieve a temperature gradient between the fruits and the background by Stajnko et al. (2004).
Correlation coefficients (R2) of 0.83–0.88 were obtained from the developed
algorithm and actual manual measurement. The R2 was also found to be increasing with growing maturity stages. The R2 of 0.68 and 0.70 was obtained for fruit
diameter analysis, and it was related to the fruit’s color and size during the maturity stages. From this pilot study, it was inferred that a real-time orchard operation
using thermal imaging technique would be possible.
Bulanon et al. (2008) suggested that examining the thermal variations in citrus canopy could be employed in automatic fruit detection for harvesting citrus
(Hamlin variety) (Fig. 8.6). The acquired thermal images were calibrated for fruit
emissivity (0.9), ambient temperature, RH, and the reflected temperature. Then,
the images demonstrated a relatively large temperature gradient, especially in the
afternoon till midnight.
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Fig. 8.6 Image acquisition system and setup (Reproduced from Bulanon et al. 2008 with permission from Elsevier Ltd.)
Maturity Detection
Maturation indicates the readiness of the produce (fruits or vegetables) for harvest. Manual inspection for maturity of produces in the whole farm is quite time
consuming and biased. Danno et al. (1980) used an infrared imaging system to
evaluate the maturity grades of fruits of Japanese persimmon (Disopyros kaki L.,
cv. Hiratanenashi), Japanese pear (Pyrus serotina Rehder var. culta Rehder, cv.
Nijisseiki), and tomato (Lycopersicon esculentum Mill, cv. Yūyake B-go). Three
grades of samples (immature, mature, and over-ripe) were stored in thermo-regulated rooms at 30 and 5 °C for more than 24 h before imaging. The infrared radiation emitted from the samples was captured by an infrared camera. It was inferred
that the surface temperature of the immature fruits stored at lower temperature
(5 °C) prior to analysis was slightly higher than the matured and the over-ripe
groups. Offermann et al. (1998) observed that fruits can be distinguished based on
maturity by measuring their maximum skin temperature by energizing the samples
by a short and intense pulse of light (for 5 min) using pulsed infrared thermography.
Bruise and Other Surface Defects Detection
Proper handling during harvest, post-harvest, and storage is essential to maintain
the quality and prevention from diseases. Bruises and other mechanical damage
on fruits and vegetables affect the surface quality and also provide access to deteriorating microorganisms resulting in rots and yield loss (Bachmann and Earles
2000). Therefore, in addition to safe handling, an automated mechanism to identify and remove the contaminated produces would help in preventing cross-contamination with healthy produces.
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M. Teena and A. Manickavasagan
Detection of bruise and other defects of fruit and vegetables is a major ­problem
in maintaining post-harvest quality. Visual inspection method is a time-consuming
process producing inconsistent results. Danno et al. (1978) applied thermal imaging to examine the effect of temperature distribution on artificially damaged
apple (Roll’s Janet), Satsuma mandarin and Natsudaidai citrus stored in thermoregulated rooms (10 and 30 °C). Two types of bruises were prepared on the
fruits, namely pressed bruise (by compressing with a steel coaxial cylinder) and
scratched bruise (made by scratching with sand paper). The surface temperature
at the bruised areas was slightly less than the normal area of the fruit, and bruises
with a temperature change of 0.2 °C were detected using thermal imaging. Varith
et al. (2003) reported that 1–2 °C of temperature noticed in detecting bruises created on apples (red ‘Delicious’, ‘Fuji,’ and ‘McIntosh’) after holding at 26 °C and
50 % RH for 48 h.
Pulsed-phase thermography (PPT) was used to detect early bruise defect in
apples (‘Jonagold’, ‘Champion,’ and ‘Gloster’) which were invisible to passive
thermography (Baranowski and Mazurek 2009; Baranowski et al. 2009). The
fast Fourier transform was used to detect the heat response to defects occurring
at different depths on the fruit (Baranowski et al. 2012). Unlike apples, surface
bruises such as soft spots in tomatoes (Lycopersicon esculentum) are almost invisible. Microwaving tomatoes for 7–15 s before thermal imaging could differentiate
between the bruised and undamaged tissues (­Van-Linden et al. 2003).
Watercore defect is the formation of a translucent tissue in certain apple cultivars when the intercellular air spaces of the entire fruit become filled with fluid.
Color vision techniques could only detect fruits with severe injury; therefore,
infrared thermography was used by Baranowski et al. (2008) for this application.
A good correlation was obtained between the derivative of apple (‘Gloster’) temperature in time per apple mass and the fruit density in watercore affected and
unaffected fruits using passive thermography (Fig. 8.7).
Steam Disinfection
Disinfection of harvested vegetables prior to storage is necessary to prevent insect
and microbial infestation in store rooms. Steam disinfection method has been
replacing the chemical fumigants in most of the industries. However, duration of
steam exposure should be significantly monitored to prevent excess heat absorbance by the produce followed by damage to internal tissues. A real-time thermal
imaging system placed inside a steam treatment chamber to monitor temperatures
on the carrot surface (Daucus carota L.) proved to be an efficient technique for
optimizing heat level and uniformity over the entire carrot surface. Steam treatment (3 s) immediately after hydro-cooling (4 °C for 10 min) caused less damage to the carrot tissue while reducing 60 % of soft rot and a minor reduction in
sprouting after cold storage (Gan-Mor et al. 2011) (Fig. 8.8).
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Fig. 8.7 Sequence of thermograms of ‘Gloster’ apples during the heating process (Reproduced
from Baranowski et al. 2008 with permission from Elsevier Ltd.)
Freezing Effect
Freezing technique is a valuable technique in food preservation. Understanding
the crystallization process and the thermodynamic properties of water is necessary to improve the control of freezing technique. The temperature distribution
of raw potato surface (Solanum tuberosum L. cv. Melody) was measured during the freezing period using an infrared thermal camera (7.5–13 μm) by Cuibus
et al. (2013). The volume, moisture content, water activity, microstructure, and the
dielectric spectra of potato samples were measured before and after freezing. The
results showed that infrared thermography and dielectric properties could be used
as a non-destructive tool for controlling the freezing process of potato.
The structural integrity of muscle tissue in meat changes during freezing
results in reduced nutritive and organoleptic qualities. Presently, meat industry
ensures appropriate control of temperature and cooling rate during freezing process to maintain quality and safety. Infrared thermography has become popular in this area due to its fast, real-time response and ease of handling. Balaguer et
al. (2013) used infrared camera to image the frozen meat samples of pork
(Longissimusdorsi) from room temperature to −20 °C at a cooling rate of 0.1
°C/min. A certified reference emitter emissivity label of known emissivity (ε = 0.95)
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Fig. 8.8 Typical thermal images of a carrot cross section after hydro-cooling to 4 °C: a immediately after hydro-cooling; b after an additional 8 min at RT; c after treatment of the cold carrots
by precise steam application; d after treatment of the 8-min RT carrots by precise steam application. Carrots were transferred through the steam treatment chamber, mounted with heat-radiation
reflectors, for a passage time of 3 s. The pressures and temperatures in the boiler and steam line
were kept below 0.4 MPa and 120 °C, respectively. The scale on the right provides a key for the
temperature level at each point in the cross section. Immediately after the heat treatment, the
carrots were cut in half, stuck on a nail in front of a hot background and imaged. This procedure
typically lasted 10 s which cause temperature drop below 19 °C in all treatments (Reproduced
from Gan-Mor et al. 2011 with permission from Elsevier Ltd.)
was also used. The results showed that meat emissivity obtained by thermal imaging
can correctly represent the temperature distribution of the meat surface.
Insect Infestation
Cowpea seed beetle (Callosobruchus maculatus (F.)) infestation is one of the
major reasons for the losses pulse loss during storage. Conventional techniques
to detect insect infestation are destructive and time intensive processes. Thermal
images of uninfested, infested (by egg, larval, pupa stages of C. maculatus (F.)),
and completely infested mung beans (hollowed out) were captured using an infrared thermal camera by Chelladurai et al. (2012). Classification models (LDA and
QDA) were developed based on the extracted features from the thermal images
of mung beans. The QDA classification model showed more than 80 % accuracy in classifying mung beans infested with initial stages of C. maculates from
uninfested ones. In a similar study, Manickavasagan et al. (2008a) also achieved
77–83 % for the six developmental stages (four larval instars, pupae, and adults)
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Fig. 8.9 Infrared
measurement obtained in
THI format (Reproduced
from Hahn et al. 2006 with
permission from Canadian
Society for Bioengineering)
of Cryptolestes ferrugineus infestation under the seed coat on the germ of the
wheat kernels using thermal imaging system.
Microbial Infection
Detection of microbial contamination in food products is important as some pathogens lead to lethal effects in human being after consumption. The traditional
detection techniques for microbial contamination in food industries are time consuming and labor intensive. Hence, there is an increasing need for rapid, sensitive,
and non-destructive detection methods for microbial contamination in food industries. Hahn et al. (2006) used a thermal camera to detect the early growing stage of
Escherichia coli (E. coli). The heat produced by the bacteria grown on Levine agar
was measured using a thermal camera (Fig. 8.9).
Thermal imaging yielded faster prediction of bacterial colonies than traditional
techniques with 100 % prediction accuracy. It was reported that the minimum time
required for detecting microbial contamination was 5 h.
Fungal infections by Aspergillus glaucus group, Aspergillus niger van Tieghem,
and Penicillium spp. in stored wheat was successfully detected by thermal imaging
(Chelladurai et al. 2010). The images of grain samples were captured after heating by a plate heater (90 °C) for 180 s and further cooled by ambient air for 30 s.
The classification models using temperature features yielded more than 97 and
96 % for detecting infected samples using LDA and QDA analyses, respectively.
However, it was not possible to detect the fungal species with high accuracy using
thermal imaging.
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Aeration System Management
The quality of produce can be maintained during storage if temperature, humidity, and ambient air flow are favorably monitored. For instance, potatoes are maintained at a desirable temperature of 4–5 °C for up to 5–8 months in free convective
ventilated stores without additional ventilation. The issues related to this type of
storage are mainly concerned with early sprouting, shrinkage, and weight loss. A
thermographic imaging system was applied to detect narrow differences of surface
temperatures in the potato boxes stored in the free convective ventilation (Geyer
and Gottschalk 2008). The infrared camera worked at a wavelength range of 7.5–
13 μm. The imaging system was successfully used to maintain the temperature of
potato in order to minimize the quality degradation during storage.
Hot Spot
In general, the temperature profile of grains stored in a silo is monitored using
thermocouples. Manickavasagan et al. (2006b) evaluated the potential of thermal
imaging to identify a hot spot in an experimental silo filled with barley. An artificial heat source was placed at nine locations inside the grain bulk and set at four
temperature levels (30, 40, 50, and 60 °C) in each location. The outer surface of
the silo wall and the top surface of the grain bulk were thermally imaged up to
48 h at each treatment. The hot spot was detected from the thermal images of the
silo wall and grain bulk (as a high-temperature region) when it was located 0.3 m
from the silo wall and 0.3 m below the grain surface, respectively (Fig. 8.10). The
hot spot was not detected on the thermal images of the silo wall during windy time
and immediately after wind. It was also reported that thermal imaging cannot be
used as an independent method to monitor the grain temperature in a silo.
Grading
In general, grading of agricultural and food products aims to improve the product
uniformity within a particular grade and serves as the basis for price. For most of
the commodities, the grading has been carried out manually by skilled workers for
long time. However, recently this process has been conquered by various types of
equipment.
Consistent efforts have been made to computer vision technology for noninvasive, non-destructive classification of grains, to improve its performance.
An infrared thermal imaging system was developed to identify the eight western
Canadian wheat classes (14 % moisture content, wet basis) by Manickavasagan
et al. (2008b, 2010). The temperatures of the surface of the grain bed were imaged
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Fig. 8.10 Thermal images of a steel silo with a hot spot (60 °C) at 0.3 m from the silo
wall at different depths: a without hot spot; b 0.3 m; c 0.6 m; d 0.9 m (Reproduced from
Manickavasagan et al. 2006b with permission from American Society of Agricultural and
Biological Engineers)
before heating (T1), after heating for 180 s (T2), and after cooling for 30 s (T3).
Eventually, T2 and T3 were significantly different for the eight wheat classes
(α = 0.05). The overall classification accuracies of an eight-class model, red-class
model (four classes), white-class model (four classes), and pairwise (two-class
model) comparisons using a quadratic discriminant method were 76, 87, 79, and
95 %, respectively. While developing thermal imaging techniques for varietal classification, several factors such as growing season, defects, and kernel size should
also be accounted.
Surface Quality Detection
The surface qualities of several agricultural produces could be analyzed by measuring certain thermal processes such as transpiration and respiration. The possibilities and limitations of thermal imaging systems to detect post-harvest quality
changes of fruits and vegetables were demonstrated by Linke et al. (2000). The
external freshness of the produce was determined by transpiration resistance values. It was noted that the transpiration resistance values increased with the aging
of the produce.
Similar experiments were conducted on two apple cultivars (Jonagored and
Elsh) of harvested at two different dates by Veraverbeke et al. (2006). The quality
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assessment study was conducted after controlled atmosphere storage from 4 to
8 months. The surface cooling rate and the final surface temperature were obtained
for each fruit from the thermographic image captured during cooling from 12 to
1 °C. The cooling rate was significantly different between the cultivars, harvesting
date, and storage conditions.
Temperature Measurement
The temperature profiles of products can be highlighted for any abnormalities
using thermal images. Costa et al. (2007) investigated the potential of infrared
imaging to evaluate pork and ham quality 20 min after stunning on the slaughterline. After imaging, the carcasses were chilled for 24 h at a temperature of 0–4 °C.
A significant difference in the surface temperature in hams was observed based on
the fat cover score. A high surface temperature was observed in low-fat-covered
hams which may be due to poor thermal insulation when compared to the high-fatcovered meat.
An accurate non-invasive system to assess the internal temperature of frozen or
thawed meat products is still lacking. The variability in the inside temperature of
cooked meat poses a serious threat to food safety. Berry (2006) conducted a variability study on internal temperature immediately after cooking the beef patties using
infrared thermography. It was observed that during cooking, the frozen beef patties shrunk in thickness and distorted in shape. Hence, the internal temperature was
higher and more consistent in patties cooked from the thawed state than the frozen
state. Thawing the meat also helped in achieving the brown color on cooking. In a
similar study, external temperature of cooked chicken meat obtained from infrared
images was associated with the internal temperature measured by conventional thermocouples. This combined multi-layer neural network method was able to estimate
internal temperature with a standard error of ±1.07 °C in 540 s after cooking for
3 min (Ibarra et al. 2000). This method was recommended to use in conveyor belttype cooking of chicken meat or other similar products to measure its doneness.
Drying
The drying of fruits and vegetables is carried out to prevent microbial activities
and extend its shelf life for long-term storage periods. Several factors such as air
flow velocity, temperature, humidity, and state of the produce (surface condition,
form, maturity, and so on) influence the water evaporation process during drying.
Loss of excess moisture will result in losses of weight, quality, and freshness.
For example, in citrus industry, occurrence of dried orange peel drying
(absence of water on surface) must be avoided because it contributes to fruit surface damage. Generally, citrus surface driers use high-temperature processes or
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Fig. 8.11 The citrus surface temperature development throughout surface drying. Drying
time was 3.2 min. This experiment corresponded to drying of orange wax coating with
2.43 × 10−2 kg/m2 (Swh), drying at 25 °C air temperature, and 1 m/s air velocity where the adiabatic saturation temperature was 15.1 °C and wet bulb temperature was 19.2 (Reproduced from
Fito et al. 2004 with permission from Elsevier Ltd.)
excessive holding time which affects the sensorial quality and shelf life of the
fruit. A new system was developed by Fito et al. (2004) that control the surface
drying time of oranges (Valencia Late variety) using thermal imaging techniques
(Fig. 8.11). Wax-coated oranges were dried at 20, 25, and 35 °C with air at 1, 1.5,
and 2 m/s velocity. The drying time was established by keeping temperature at any
point on the surface of the fruit below a critical value.
During meat drying, it is crucial to understand the critical points that cause
severe quality deterioration. Triffano-Schiffo et al. (2013) used an infrared thermography in ham drying process to control the critical points. A reference material
of known emissivity (ε = 0.95) was placed next to the sample, and the infrared
emissions were detected by an infrared camera (spectral range of 7.5–13 μm). In
addition to measurement of emissivity during drying, mass, moisture, volume, and
water activity for each sample were measured after drying process. The relationship of meat emissivity with its moisture content was marked in this study.
Non-uniform Heating
Thermal imaging can be used as an effective tool to evaluate the heating pattern
and uniformity of novel driers such as microwaves.
Although microwaves have potential to heat the product quickly, non-uniform
heating pattern results in the production of hot and cold spots which results in the
quality degradation.
Manickavasagan et al. (2006a) studied the non-uniformity of surface temperatures of grain after microwave treatment. Non-uniform heating pattern was
observed in the tested three grain types (wheat, barley, and canola) at different
moisture levels, microwave powers, and treatment time. It was reported that the
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temperature difference (ΔT) was in the range of 7.2–78.9 °C, 3.4–59.2 °C, and
9.7–72.8 °C for barley, canola, and wheat, respectively.
The effect of hot spot on germination percentage of Canadian hard red spring
wheat samples collected from the hot spot and the normal heated zones after microwave heating was studied by Manickavasagan et al. (2007). The hot spots and the
normal heated zones were determined from the live thermal images immediately
after the microwave treatment. The germination percentages of samples collected
from the hot spot were significantly lower (α = 0.05) than the normal heated zone
at all moisture (12, 15, 18, and 21 % wet basis) and power (100, 200, 300, 400, and
500 W) levels employed. At the highest power level treatment (500 W for 28 s), the
germination percentage became zero in the hot-spot zones, while it was 4–33 % in
the normal heating zone. In a similar study with bulk rye, oats, and sunflower seeds,
Vadivambal et al. (2009) reported that the temperature difference between hot and
cold spots varied between 23 and 62 °C, 7 and 25 °C, and 7 and 29 °C, respectively.
The potential of continuous microwave heating to reduce the moisture content in food products to maintain quality was investigated by Boldor et al. (2005).
This study analyzed the effect of microwave energy level on temperature profiles
and moisture removal of farmer stock in-shell uncured peanuts (25–45 % MC dry
basis) in a continuous wave applicator using 915 MHz microwaves. An electric
heater was set to maintain an ambient temperature of 25 °C inside the system. To
examine the spatial temperature distribution of the surface of the peanut bed, three
systems were used: fiber-optic probes (connected to a multi-channel fiber-optic
signal conditioner), thermocouples (placed at various distances along the waveguide), and a thermal camera (placed at the exit of the microwave curing chamber). It was reported that the surface temperatures of the peanut bed measured at
the exit of the microwave chamber were uniformly distributed.
Manickavasagan et al. (2009) evaluated the non-uniformity of heating of readyto-eat chicken pies after heating in a domestic microwave oven. The surface temperature and internal temperatures of the pie after heating at different locations of
microwave cavity were measured using infrared camera and thermocouple, respectively. The ΔT was in the range of 31.6–130.5 °C on the surface and 10.7–76.1 °C
inside the pie. It was noted that the non-uniformity on the surface was significantly
lower on the pie placed on the turntable (Fig. 8.12).
Foreign Substances Detection
In food industries, the foreign materials are most undesired materials in the food
products. At present, these materials are detected mostly by mechanical, optical,
and ultrasonic methods. However, there are some specific substances which cannot be detected in the current methods. Meinlschmidt and Margner (2002, 2003)
proposed an automatic detection mechanism using thermal imaging to detect foreign substances by measuring the difference in emissivity coefficients or heat conductivities of different food products (Fig. 8.13). In this approach, the products on
the conveyer should be heated or cooled before taking images. The differences in
8 Thermal Infrared Imaging
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Fig. 8.12 Thermal imaging of a pie after microwave heating (Reproduced from Manickavasagan
et al. 2009 with permission from Canadian Society for Bioengineering)
Fig. 8.13 Experimental on-line setup for detecting foreign bodies in food (Reproduced from
Meinlschmidt and Margner 2003 with permission from SPIE)
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M. Teena and A. Manickavasagan
the rate of cooling or heating between the food substance and the foreign material could be used to identify the foreign materials (Manickavasagan and Jayas
2007). Warmann and Margner (2005) developed a system with thermal imaging to
classify contaminants in hazelnuts using thresholding, texture analysis, and fuzzy
logic algorithms.
Conclusions
Thermal imaging is an emerging tool with several applications to preserve food
quality and safety. The thermal imaging technique plays a major role in temperature mapping of various food products in industries and is gaining momentum.
With improved technology, thermal imaging systems become more consistent,
accessible, and precise and cost-efficient tool in food applications. The thermal
imaging method has potential to be used in many pre-harvest and post-harvest
operations of agriculture. The non-contact, non-destructive nature of thermal
imaging along with rapid online usability is the major reasons for the fast growing demand for this technique in various applications. Since the thermal behavior of plants and agricultural products vary with climatic conditions, it may be
required to develop different application protocols based on the process and the
product applied. Most of the applications of thermal imaging discussed are still
under investigation; therefore, advanced research should meet the requirements for
real-time industrial quality evaluation purposes.
Acknowledgment We thank The Research Council (TRC) of Sultanate of Oman for funding
this study (Project No. RC/AGR/SWAE/11/01—Development of Computer Vision Technology
for Quality Assessment of Dates in Oman).
References
Alchanatis V, Cohen Y, Cohen S, Moller M, Sprinstin M, Meron M, Tsipris J, Saranga Y, Sela E
(2010) Evaluation of different approaches for estimating and mapping crop water status in
cotton with thermal imaging. Precis Agric 11:27–41
Bachmann J, Earles R (2000) Postharvest handling of fruits and vegetables—horticulture technical note. ATTRA 1:1–19
Balaguer N, Castro-Giráldez M, Fito PJ (2013) Study of pork meat freezing process by infrared
thermography. In: Inside food symposium, Leuven, Belgium
Baranowski P, Lipecki J, Mazurek W, Walczak RT (2008) Detection of watercore in ‘Gloster’
apples using thermography. Postharvest Biol Technol 47:358–366
Baranowski P, Mazurek W (2009) Detection of physiological disorders and mechanical defects in
apples using thermography. Int Agrophys 23:9–17
Baranowski P, Mazurek W, Witkowska-Walczak B, Sławinski C (2009) Detection of early apple
bruises using pulsed-phase thermography. Postharvest Biol Technol 53:91–100
Baranowski P, Mazurek W, Wozniak J, Majewska U (2012) Detection of early bruises in apples
using hyper spectral data and thermal imaging. J Food Eng 110:345–355
8 Thermal Infrared Imaging
171
Berry BW (2006) Use of infrared thermography to assess temperature variability in beef patties
cooked from the frozen and thawed states. Foodservice Res Int 12:255–262
Boldor D, Sanders TH, Swartzel KR, Simunovic J (2005) Thermal profiles and moisture loss
during continuous microwave drying of peanuts. Peanut Sci 32:32–41
Bulanon DM, Burks TF, Alchanatis V (2008) Study on temporal variation in citrus canopy using
thermal imaging for citrus fruit detection. Biosyst Eng 101:161–171
Chaerle L, Caeneghem WV, Messens E, Lambers H, Montagu MV, Straeten DVD (1999)
Presymptomatic visualization of plant—virus interactions by thermography. Nat Biotechnol
17:813–816
Chaerle L, Boever FD, Montagu MV, Straeten DVD (2001) Thermographic visualization of cell
death in tobacco and Arabidopsis. Plant Cell Environ 24:15–25
Chelladurai V, Jayas DS, White NDG (2010) Thermal imaging for detecting fungal infection in
stored wheat. J Stored Prod Res 46:174–179
Chelladurai V, Kaliramesh S, Jayas DS (2012) Detection of Callosobruchus maculatus (F.)
infestation in mung bean (Vigna radiata) using thermal imaging technique. In: NABECCSBE/SCGAB 2012 joint meeting and technical conference northeast agricultural and biological engineering conference, Orillia, Ontario
Cohen Y, Alchanatis V, Meron M, Saranga Y, Tsipris J (2005) Estimation of leaf water potential
by thermal imagery and spatial analysis. J Exp Bot 56:1843–1852
Costa NL, Stelletta C, Cannizzo C, Gianesella M, Fiego PLD, Morgante M (2007) The use of
thermography on the slaughter-line for the assessment of pork and raw ham quality. Ital
J Anim Sci 6:704–706
Cuibus L, Castro-Giráldez M, Fito PJ, Fabbri A (2013) Application of infrared thermography and
dielectric spectroscopy for controlling freezing process of raw potato. In: Inside food symposium, Leuven, Belgium
Danno A, Miyazato M, Ishiguro E (1978) Quality evaluation of agricultural products by infrared
imaging method: I. Grading of fruits for bruise and other surface defects. Memoirs of the faculty of agriculture, Kagoshima University, Kagoshima, vol 14, pp 123–138
Danno A, Miyazato M, Ishiguro E (1980) Quality evaluation of agricultural products by infrared
imaging method: III. Maturity evaluation of fruits and vegetable. Memoirs of the faculty of
agriculture, Kagoshima University, Kagoshima, vol 16, pp 157–164
Fito PJ, Ortolá MD, De los Reyes R, Fito P, De los Reyes E (2004) Control of citrus surface drying by image analysis of infrared thermography. J Food Eng 61:287–290
FLIR (2012) The ultimate infrared handbook for Rand D professionals. FLIR Systems
Incorporations, NH
Fuller MP, Wisniewski M (1998) The use of infrared thermal imaging in the study of ice nucleation and freezing of plants. J Therm Biol 23:81–89
Gan-Mor S, Regev R, Levi A, Eshel D (2011) Adapted thermal imaging for the development of
postharvest precision steam-disinfection technology for carrots. Postharvest Biol Technol
59:265–271
Geyer S, Gottschalk K (2008) Infrared thermography to monitor natural ventilation during storage of potatoes. Agric Eng Int CIGR J X:1–14
Gowen AA, Tiwari BK, Cullen PJ, McDonnell K, O’Donnell CP (2010) Applications of thermal imaging in food quality and safety assessment—review. Trends Food Sci Technol
21:190–200
Grant OM, Davies MJ, James CM, Johnson AW, Leinonen I, Simpson DW (2012) Thermal imaging and carbon isotope composition indicate variation amongst strawberry (Fragaria × ananassa) cultivars in stomatal conductance and water use efficiency. Environ Exp Bot 76:7–15
Grant OM, Tronina L, Jones HG, Chaves MM (2007) Exploring thermal imaging variables for
the detection of stress responses in grapevine under different irrigation regimes. J Exp Bot
58:815–825
Hahn F, Hernández G, Echeverría E, Romanchick E (2006) Escherichia coli detection using thermal images. Can Biosyst Eng 48:4.7–4.13
172
M. Teena and A. Manickavasagan
Hellebrand HJ, Herppich WB, Beuche H, Dammer KH, Linke M, Flath K (2006) Investigations
of plant infections by thermal vision and NIR imaging. Int Agrophysics 20:1–10
Holst GC (2000) Common sense approach to thermal imaging. SPIE Press and JCD Publishing, FL
Ibarra JG, Tao Y, Xin H (2000) Combined IR imaging-neural network method for the estimation
of internal temperature in cooked chicken meat. Opt Eng 39:3032–3038
Infrared Training Center (2002) Course manual—level I, MA, USA
Inoue Y, Kimball BA, Jackson RD, Pinter PJ Jr, Rejinato RJ (1990) Remote estimation of leaf
transpiration rate and stomatal resistance based on infrared thermometry. Agric For Meteorol
51:21–33
Jones HG (1999) Use of thermography for quantitative studies of spatial and temporal variation
of stomatal conductance over leaf surfaces. Plant Cell Environ 22:1043–1055
Jones HG (2004) Application of thermal imaging and infrared sensing in plant physiology and
ecophysiology. Adv Bot Res 41:107–162
Jones HG, Schofield P (2008) Thermal and other remote sensing of plant stress. Gen Appl Plant
Physiol 34(1–2):19–32
Jones HG, Stoll M, Santos T, Sousa CD, Chaves MM, Grant OM (2002) Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot
53:2249–2260
Kalma JD, Jupp DLB (1990) Estimating evaporation from pasture using infrared thermometry:
evaluation of a one-layer resistance model. Agric For Meteorol 51:223–246
Lindenthal M, Steiner U, Dehne HW, Oerke EC (2005) Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Am
Phytopathological Soc 95:233–240
Linke M, Geyer M, Beuche H, Hellebrand HJ (2000) Possibilities and limits of the use of thermography for the examination of horticultural products. Agrartechnische Forschung 6, Heft
6, S:110–114
Manickavasagan A, Jayas DS, White NDG (2006a) Non-uniformity of surface temperatures of grain after microwave treatment in an industrial microwave drier. Drying Technol
24:1559–1567
Manickavasagan A, Jayas DS, White NDG, Jiuan F (2006b) Thermal imaging of a stored grain
silo to detect a hot spot. Appl Eng Agric 22:891–897
Manickavasagan A, Jayas DS (2007) Infrared thermal imaging for agricultural and food applications. Stewart Postharvest Rev 5:1–8
Manickavasagan A, Jayas DS, White NDG (2007) Germination of wheat grains from uneven
microwave heating in an industrial microwave dryer. Can Biosyst Eng 49:3.23–3.27
Manickavasagan A, Jayas DS, White NDG (2008a) Thermal imaging to detect infestation by
Cryptolestes ferrugineus inside wheat kernels. J Stored Prod Res 44:186–192
Manickavasagan A, Jayas DS, White NDG, Paliwal J (2008b) Wheat class identification using
thermal imaging: A potential innovative technique. Trans ASAB 51:649–651
Manickavasagan A, Jayas DS, Vadivambal R (2009) Non-uniform microwave heating of
­ready-to-eat chicken pies. Can Biosyst Eng 51:3.39–3.44
Manickavasagan A, Jayas DS, White NDG, Paliwal J (2010) Wheat class identification using
thermal imaging. Food Bioprocess Technol 3:450–460
Meinlschmidt P, Maergner V (2002) Detection of foreign substances in food using thermography.
In: Conference thermo sense XXIV, Orlando, Florida, USA, pp 565–571
Meinlschmidt P, Margner V (2003) Thermographic techniques and adopted algorithms for automatic detection of foreign bodies in food. In: Proceedings of thermo sense XXV, Bellingham,
WA: SPIE 2003, pp 168–177
Meola C, Carlomagno GM (2004) Recent advances in the use of infrared thermography—review
article. Meas Sci Technol 15:R27–R58
Meron M, Sprintsin M, Tsipris J, Alchanatis V, Cohen Y (2013) Foliage temperature extraction
from thermal imagery for crop water stress determination. Precision Agric. doi:10.1007/
s11119-013-9310-0
8 Thermal Infrared Imaging
173
Moller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, Ostrovsky V, Sprintsin M, Cohen
S (2007) Use of thermal and visible imagery for estimating crop water status of irrigated
grapevine. J Exp Bot 58:827–838
Oerke EC, Steiner U, Dehne HW, Lindenthal M (2006) Thermal imaging of cucumber leaves
affected by downy mildew and environmental conditions. J Exp Bot 57:2121–2132
Offermann S, Bicanic D, Krapez JC, Balageas D, Gerkema E, Chirtoc M, Egee M, Keijzer K,
Jalink H (1998) Infrared transient thermography for non-contact, nondestructive inspection
of whole and dissected apples and of cherry tomatoes at different maturity stages. Instrum
Sci Technol 26(2–3):145–155
Pearce RS, Fuller MP (2001) Freezing of barley studied by infrared video thermography. Plant
Physiol 125:227–240
Sankaran S, Maja JM, Buchanon S, Ehsani R (2013) Huanglongbing (citrus greening) detection
using visible, near infrared and thermal imaging techniques. Sensors 13:2117–2130
Stajnko D, Lakota M, Hoĉevar M (2004) Estimation of number and diameter of apple fruits in
an orchard during the growing season by thermal imaging. Comput Electron Agric 42:31–42
Sugiura R, Noguchi N, Ishii K (2007) Correction of low-altitude thermal images applied to estimating soil water status. Biosyst Eng 96:301–313
Triffano-Schiffo MV, Castro-Giráldez M, Fito PJ (2013) Study of ham drying kinetics by infrared thermography. In: Inside food symposium, Leuven, Belgium
Vadivambal R, Chelladurai V, Jayas DS, White NDG (2010) Detection of sprout-damaged wheat
using thermal imaging. Appl Eng Agric 26:999–1004
Vadivambal R, Chelladurai V, Jayas DS, White NDG (2011) Determination of sprout-damaged
barley using thermal imaging. Agric Eng Int CIGR J 13:1–9
Vadivambal R, Jayas DS, Chelladurai V, White NDG (2009) Preliminary study of surface temperature distribution during microwave heating of cereals and oilseed. Can Biosyst Eng
51:3.45–3.52
Van-Linden V, Vereycken R, Bravo C, Ramon H, Baerdemaeker JD (2003) Detection technique
for tomato bruise damage by thermal imaging. Acta Hortic (ISHS) 599:389–394
Varith J, Hyde GM, Baritelle AL, Fellman JK, Sattabongkot T (2003) Non-contact bruise detection in apples by thermal imaging. Innovative Food Sci Emerg Technol 4:211–218
Veraverbeke EA, Verboven P, Lammertyn J, Cronje P, Baerdemaeker JD, Nicolai BM (2006)
Thermographic surface quality evaluation of apple. J Food Eng 77:162–168
Warmann C, Margner V (2005) Quality control of hazel nuts using thermographic image processing.
In: IAPR conference on machine vision applications, Tsukuba Science City, Japan
Chapter 9
Microwave Imaging
Massimo Donelli
Introduction
Microwaves are electromagnetic waves ranging from approximately 1–300 GHz
in frequency; older classifications and standards include lower frequencies up
to 300 MHz including UHF and EHF (millimetric waves) (Pozar 2011; Scott
1993; Gupta 1980; Sisodia and Gupta 2004). The most widespread applications
are within the 1–40 GHz range (Pozar 2011). Microwaves are quite directive and
particularly suitable for point-to-point communication (Roddy 2001), and not for
broadcast communication. Moreover, microwaves are not reflected by ionosphere
and particularly suitable for satellite communication applications. For this reason, microwaves are extensively used in satellite and spacecraft communication,
and most of the data transmitted with radio, television, and phones are delivered
toward long distances by microwaves considering ground stations and satellites. It
is worth noting that three satellites are enough to cover the whole globe. Table 9.1
summarizes the classification of microwave frequency bands provided by the
Radio Society of Great Britain (RSGB) within their main applications.
The ways to generate microwaves depend on the power required for the application at hand. In particular to generate high-power microwaves, specialized
vacuum tubes are used. These vacuum tubes operate considering the ballistic
motion of electrons in vacuum under the influence of controlling electric or magnetic fields. The most diffused vacuum tubes are the magnetron (commonly used
in domestic and industrial microwave ovens), klystron, and traveling-wave tube
(TWT). These devices to work properly require high voltages and a magnetostatic
field usually generated with a strong magnet. The magnetron was used for the
first time during the second world war in an English radar mounted onboard of a
M. Donelli (*)
Department of Information Engineering and Computer Science, Polo Scientifico e
Tecnologico Fabio Ferrari, University of Trento, Via Sommarive 9, Trento, Italy
e-mail: donelli@disi.unitn.it
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_9, © Springer-Verlag Berlin Heidelberg 2014
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Table 9.1 Most used microwave frequency bands and related applications
Wave band
Frequency
(GHz)
Application
L-band
S-band
1–2
2–4
C-band
X-band
4–8
8–10
Ku-band
K-band
Ka-band
Q-band
U-band
12–18
18–26
26–40
33–50
40–60
GPS, GSM, radio amateur
Microwave ovens, microwave devices for communications,
ZigBee, WiFi, satellite communications
Medium- and long-range radio communications
Weather radar, satellite communications, terrestrial broadcast
communications, radio astronomy
Satellite communications
Radar, satellite communications, radio astronomy
Satellite communications
Radio astronomy, automotive radar, satellite communications
Satellite and high-speed terrestrial communications
bomber. Low-power microwaves can be generated by means of solid-state devices
such as the field-effect transistor (for lower frequencies for the L and S bands),
tunnel, Gunn, and IMPATT diodes. Low-power sources are available in many
laboratory instruments, embedded radar modules, and in most computer card for
wireless LAN.
General Applications of Microwaves
Terrestrial Communications
The gain of antennas is proportional to the electrical size of the antenna which is
proportional to the wavelength. At higher frequencies, due to the reduced wavelength,
it is possible to obtain high antenna gain with a reduced size; this aspect is particularly important for the development of miniaturized microwave systems. Moreover,
working at high frequencies permits to obtain more bandwidth and consequently more
information carrying capability. The bandwidth in the last decades became critically
important because the available spectrum is quite crowed. The majority of modern
data transfers are made wireless by using microwaves (Roddy 2001; Ahmad 2005;
Morinaga et al. 2002; Bensky 2004); most of the wireless LAN protocols, such as
Bluetooth and the IEEE 802.11 specifications, use microwaves in the S and C bands,
the 2.4 GHz ISM band, although 802.11a uses ISM band and frequencies in the
5 GHz range. Medium- and long-range wireless Internet access services (up to about
30 km) have been used for almost a decade in many countries in the S and C bands
(at 3.5 and 4.0 GHz). The metropolitan area network (MAN) protocols, like WiMAX
(Worldwide Interoperability for Microwave Access), are based on standards like IEEE
802.16, designed to operate between 2 and 11 GHz, while commercial implementations are in the range between 2.3 and 5.8 GHz. Most of mobile phone networks in
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the world, like GSM, use the low-microwave/high-UHF frequencies in the L-band
(around 1.8 and 1.9 GHz). Microwaves are also used for broadcasting telecommunication transmissions because, due to their short wavelength, it is possible to use highly
directional antennas that are smaller and therefore more practical with respect to their
counterparts at longer wavelengths (lower frequencies). Moreover, as told before,
there is also more bandwidth in the microwave spectrum than in the rest of the radio
spectrum; it is worth noticing that the usable bandwidth below 300 MHz is less than
300 MHz, while many GHz can be used above 300 MHz. For this reason, microwaves
are also used in television news to transmit a signal from a remote location to a television station by means of a local station usually located on a specially equipped van.
Satellite Communications
Microwave signals are quite directive; they travel by line of sight (LOS);
moreover, microwaves are not bent or reflected by ionosphere; for these reasons,
microwaves are particularly suitable for satellite communication where a direct
LOS link is commonly used to transfer data between a ground station and a
satellite (Kolawole 2002; Elber 2004; Goldsmith 2005) and to communicate data
between a ground station and deep space exploration satellites.
Radar Applications
Radar uses microwave radiation (Skolnik 1990; Lacomme et al. 2001; Levanon
and Mozeson 2004) to detect the distance, speed, and other characteristics of
remote targets because the radar cross section is proportional to the electrical size
of the target. This aspect makes microwaves particularly suitable for radar systems
allowing a high resolution. Today radars are widely used for applications such as
weather forecasting, navigation of ships, airplane, and others vehicles, Doppler
radars being common for the detection of the velocity limits on highways, to avoid
collisions between vehicles and also as sensor for homeland alarms. Radar is also
used for remote sensing application and for deep space exploration; a map of the
Venus surface has been obtained with a synthetic aperture radar which has permitted to see beyond the dense numbs of Venus atmosphere.
Radio Astronomy
Radio astronomy is a subfield of astronomy that studies the radio emission of
celestial objects (Thompson et al. 2004; Wilson et al. 2009). The first detection
of radio waves from an astronomical object was made in the early 1930s, when an
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electromagnetic radiation coming from the Milky Way was observed. In particular,
radio astronomy is conducted with radio telescopes which are aimed at detecting
the naturally occurring microwave radiation emitted by deep space objects such
as planets, stars and galaxies, as well as entirely new classes of objects, such as
radio galaxies, quasars, pulsars, and masers. Radio telescopes make use of large
antennas that are either used singularly or organized into array of linked telescopes
utilizing the techniques of aperture synthesis and radio interferometry.
Microwave Heating
The advantages of the use of microwaves for heating and cooking foods are quite
clear (Bengtsson and Ohisson 1974; Metaxas 1991; Varith et al. 2007; Risman and
Celuch-Marcysiack 2000; Guven, 2006; Tirawanichakul et al. 2011; Dunaeva and
Manturow 2010). The microwave energy interacts with the polar molecules and
ions belonging to the foods. The molecules and ions presenting a polar structure
will rotate or collide following the alternating electromagnetic field, and consequently, they convert the microwave energy into heat useful for cooking, defrosting,
or reheating foods. Foods present a high percentage of water; water molecule is an
electric dipole with a positive and negative charge placed at the end of the dipole.
The water dipoles tend to orient themselves following the direction of the electromagnetic field; the rotation of the water molecule produces the heat for cooking the
food. In the last decades, defrost, cooking, or reheating foods by using microwave
oven is becoming popular not only for industrial processing; indeed, most of the
restaurant and families installed microwave ovens for food. Commercial microwave
ovens are able to produce about half or one kilowatt of power, while for industrial
applications, hundreds of kilowatts are usually required.
Imaging Applications
In the following chapter, advanced applications of microwaves like microwave
imaging, for industrial as well as biomedical applications, modulated scattering
sensors and other interesting techniques useful for food and agriculture applications will be introduced and detailed. The following section explains the theory,
instruments, and techniques for microwave imaging. Three examples of microwave imaging applications for food and agriculture are also discussed. In particular, a microwave imaging technique, able to identify the composition and the shape
of biological materials, for the quality control of packed foods, and a technique
to identify the degree of ripeness of fruits, is presented. In the last part, two innovative applications of microwave are presented. In particular, the first technique
is based on the so-called modulated scattering technique (MST), and it is proposed for the real-time monitoring of complex production processes. The second
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applications concern noninvasive microwave techniques (NDE/NDT) commonly
used for the qualities assessment of industrial products, and it is applied to monitoring the properties of foods during the production phase, in particular to monitoring the aging of high-quality wheels of cheese.
Microwave Imaging
Microwave imaging techniques are used to probe inaccessible domains and to
reveal the dielectric properties of the media that they penetrate. Therefore, inverse
scattering techniques have found a variety of applications in medical diagnosis,
subsurface monitoring or geophysical inspection, and nondestructive evaluation
and testing as reported in the previous sections (Benedetti et al. 2007a, b; Donelli
and Massa 2005; Huang and Mohan 2007; Massa et al. 2005; Caorsi et al. 2002,
2003, 2004a, b, c; Donelli et al. 2005a, b, 2009; Bort et al. 2005). They are aimed
at fully characterizing the area under test in terms of positions, shapes, and complex permittivity profiles of the dielectric discontinuities (i.e., the scatterers). This
goal is reached by analyzing the scattered field reflected by the scenario under
consideration by using suitable mathematical elaboration techniques called inverse
scattering algorithms. These inversion techniques usually require high computational time and resource; the problem is solved by recasting the original problem into an optimization problem by defining a suitable cost function and then by
minimizing it with a suitable optimization algorithm. The geometry of the problem, reported in Fig. 9.1, considers a set of objects belonging a non-dissipative
homogeneous background with specific dielectric characteristics. Such objects
are located in an inaccessible area called investigation domain. The domain under
investigation is illuminated from V different directions by means of electromagnetic waves at a fixed angular frequency ω, whose electric field distribution, Einc
v(r) (v = 1, …,V), is known. The goal of the inverse scattering problem is to
reconstruct the distribution of the so-called contrast function that represents the
distribution of the dielectric characteristics in the investigation domain, from the
knowledge of the scattered field, Escatt v(m(v), collected in m(v) = 1, …, M(v),
with v = 1, …,V), positions of the measurement domain DM surrounding DI. The
physical interactions between the scatterers and the probing fields are described
through the Lippmann–Schwinger relationship. The mathematical formulation
is quite complex, and the reader is suggested to refer to the references for more
details (Caorsi et al. 2003, 2004a, b, c). Microwave imaging technique can be very
useful for food processing and agriculture applications. In particular, thanks to
the noninvasive characterization capabilities of such techniques; they are particularly useful for monitoring foods also after the packaging, without the necessity of
opening the package since microwave can easily penetrate any kind of nonmetallic
packages. Moreover, they are particularly useful to identify unwanted or extraneous bodies (like pieces of glass or plastic materials) embedded into food and not
detectable with standard metal detectors.
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Fig. 9.1 A typical microwave imaging scenario
Microwave Imaging for Food and Agricultural Application
The following three examples represent a typical application of microwave imaging
techniques for food quality assessment. In the first example, a package containing
six cookies is analyzed with microwave imaging technique. In particular, an electromagnetic field in the X-band (8–12 GHz) has been used to assess the contents of
the package. The goal is to assess whether all the cookies are inside the package and
whether their shape is preserved after the distribution. Figure 9.2 reports a reconstruction of a box of cookies, and it can be noticed in the reconstruction shown that
one cookie is missed and another is broken.
In the second experiment, a specimen of cheese (but it could be any kind of
food) is corrupted with a small piece of plastic material (this specimen can easily
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Fig. 9.2 Example of
reconstruction of a box of
cookies with a microwave
imaging technique
Fig. 9.3 Identification of an
extraneous object (a small
piece of plastic material)
embedded inside a specimen
of cheese by means of a
microwave imaging technique
pass the standard quality assessment since it is based on standard metal detectors
able to identify only small metallic bodies). The reconstruction of the dielectric
distribution of the cheese specimen, obtained with microwave imaging techniques,
is reported in Fig. 9.3. As it can be noticed from the reconstruction of Fig. 9.3, the
presence of the small piece of plastic material is clearly reported (the yellow area).
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Fig. 9.4 Schema of the monostatic continuous wave radar for the assessment of the degree of
ripeness of fruits
The previous two experiments have clearly demonstrated the potentialities and the
capabilities of microwave imaging techniques for food processing applications.
The last application considers a continuous wave radar for the assessment
of the ripeness of fruits, vegetables, and other cultures. The problem geometry
is reported in Fig. 9.4. A microwave source generates an electromagnetic wave
that is directed toward an orchard by means of a suitable transmitting antenna
(usually a directive antenna such as a pyramidal horn antenna able to provide a
high gain and directivity). The impinging electromagnetic wave is reflected by
the trees and fruits (characterized by a high water contents). The reflected electromagnetic waves contain the information related to the chemical composition
of the fruits, in particular the water and sugar contents. The scattered electromagnetic field is collected by the antenna that acts as transmitting as well as
receiving systems. The signal received by the antenna is amplified by means of
a low-noise amplifier (LNA) and then delivered to a suitable post-processing
units that make use of the same microwave imaging techniques (Benedetti et al.
2007a, b; Donelli and Massa 2005; Huang and Mohan 2007; Massa et al. 2005;
Caorsi et al. 2002, 2003, 2004a, b, c; Donelli et al. 2005a, b, 2009; Bort et al.
2005) considered in the previous examples.
Other Applications of Microwaves
In this section, two interesting applications of innovative microwave technology,
namely the MST and the microwave nondestructive evaluation and test (NDE/NDT),
are presented for monitoring the food production chain and the quality of foods.
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Modulated Scattering Technique Sensors for
Monitoring the Production of Food
In the framework of foods processing, MST probes (Bolomey and Gardiol 2001;
Tehran et al. 2010; Choi et al. 2004) could be an attractive alternative solution
to RFID systems. In particular, for all critical applications, where compactness and low power are required, the MST technique offers interesting advantages such as high communication range, flexibility, and low cost. The principal
advantage of MST probes is that they are not physically connected with the
measurement system and they do not require radiofrequency front end. Another
great advantage of MST sensors is that they can be easily integrated with existing measurement systems with limited HW modifications that do not require the
redesign of the whole system. The principle of MST is quite simple; the antenna
of the MST probe is loaded with different loads used to introduce a low-frequency modulation signal in the impinging electromagnetic wave generated by
means of a suitable reader. The reader also post-processes the backscattered
field and retrieves information from the scattered low-frequency modulation
signal provided by the tags. Since the reader generates the electromagnetic wave
that carries the information, the MST tag does not require a radio frequency
front end; this leads to a low-cost, less invasive tag particularly suitable for
measurements that require a small probe to reduce perturbations and noise in
the measurement (Liang et al. 1997). MST probes have been successfully used
for microwave imaging applications (Ostradahimi et al. 2012; Donelli et al.
2001), near-field electromagnetic measurements (Bolomey et al. 2011), material
characterization (Donelli and Franceschini 2010), and other interesting applications (Vauchamp et al. 2010). The schema of a MST probe system is reported
in Fig. 9.5; it is composed by a reader that works like a continuous wave radar
and downconverts the signal scattered by the MST tags with a coherent receiver.
The MST tag is quite simple; it consists of a small antenna loaded with different
resistive loads. An electronic switch controlled by means of a suitable control
unit changes the antenna load producing a different reflection coefficient. The
change of loads produces a modulation on the reflected electromagnetic wave
useful to transmit information.
MST sensors are more flexible and cheaper with respect to standard RFID
tags. Thanks to these properties, it is possible obtain passive sensors for the
measurement of different physical parameters typically considered in food processing. In particular, thanks to the high degree of miniaturization and the simplicity of the reader, it is possible to embed MST sensors directly in the raw
ingredients to obtain a complete monitoring during the whole production process. Figure 9.6 shows an example of application of such kind of technology for
monitoring the fermentation process during the production of continuous slab of
bread products.
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Fig. 9.5 Structure of a MST system composed by a reader and of a MST tag
Fig. 9.6 Example of application of MST sensor for the fermentation monitoring of bread
products
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Microwave Non-destructive Evaluation and Test
NDE/NDT is an interesting research area devoted to the development of advanced
sensors, measurement systems, and imaging techniques for the characterization
of materials and structures in a nondestructive fashion. Nondestructive evaluation (NDE) and testing (NDT) are mandatory in many industrial processes and
biomedical applications that require an accurate analysis of dielectric or conductive structures (e.g., industrial products and artifacts). As far as the state of the art
is concerned, ultrasonic (Rose et al. 2001) and X-rays (Leher and Liedtke 1999;
Hall et al. 1999), infrared (Favro 2001), and eddy currents (Norton and Bowler
1993) are the methodologies mainly used in dealing with NDE/NDT problems.
Recently, some “emerging” technologies like microwaves are appearing in “subsurface sensing” methods for the nondestructive evaluation (Norton and Bowler
1993; Bolomey and Joachimowicz 1994; Nyfors 2000; Bolomey 1995; 1996) and
the references therein for a general overview), and now, in some applications, the
employment of interrogating microwaves is recognized as a suitable diagnostic tool
demonstrating its advantages with respect to standard techniques (Zoughi 2000).
The main reasons of the growing interest and rapid development of microwavebased methodologies can be summarized by the following points:
1. Electromagnetic fields in the microwave range can penetrate all materials
(unless ideal conductors), and the related scattered fields are representative of
the overall volume of the object under test and not only of its surface;
2. Microwave imaging modalities are very sensitive to the water content of the
specimen (so particularly suitable for food processing techniques); and
3. Microwave sensors can be contactless with respect to the specimen, as well
(since these techniques used an electromagnetic field to retrieve information
related to the material under investigation).
Moreover, microwave technologies can be considered complementary approaches
to conventional inspection techniques guaranteeing noninvasive measurements and
avoiding collateral effects on the specimen under test (being safe non-ionizing radiations). In this framework, a further advance is represented by imaging techniques
that permit to obtain a complete image of the structure under test. Unfortunately,
these techniques are characterized by several drawbacks such as ill position and
nonlinearity as well as the presence of local minima that partially prevents their use
in industrial applications (unlike “passive” techniques). Therefore, in order to allow
an effective technological transfer in the framework of industrial processes, and in
particular in the field of food processing, other developments are mandatory. In the
following, the NDE/NDT problem will be briefly summarized.
Currently, the real-time monitoring is strongly limited by the low speed of the
reconstruction methods. Moreover, the wavelength of the probing electromagnetic source strongly limits the achievable spatial resolution or it requires high
computational costs for obtaining a detailed reconstruction. However, in the
framework of inverse scattering techniques, dealing with the detection of defects
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Fig. 9.7 NDE/NDT problem geometry
(also indicated as “cracks” in the following) in known host structures seems to
be particularly suitable for realistic food processing applications; in particular, it
can be particularly useful to detect anomalies in food such as mold, mushroom,
or other extraneous organisms which can infest foods and consequently strongly
decrease the foods quality. The geometry of a NDE/NDT problem is reported in
Fig. 9.7, where a two-dimensional area is composed by a known host medium
(called investigation domain); the area is surrounded with transmitting antennas
that illuminate the scenario with a set of electromagnetic waves. A set of probes
are placed all around the investigation domain (called observation domain). The
probes in the investigation domain collect the scattered fields. After a suitable
mathematical processing of the measured scattered field, it is possible to retrieve
the so-called object function Γ (x,y) that permits to obtain the spatial distribution of
the electric characteristics of the known background media and consequently to
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Fig. 9.8 Application of NDE/NDT techniques for the assessment of the qualities of cheese wheel
identify extraneous material. In particular, the numerical solution of such problem
requires the solution of complex differential equations. The problem is quite
complicate and usually solved transforming it into an optimization problem by
defining a suitable cost function and then by minimizing it with a suitable optimization algorithm. For more details, please refer to the following references (King
and Stiles 1984; Caorsi et al. 2004a, b, c; Donelli et al. 2005a, b, c; Benedetti et
al. 2005, 2006, 2007a, b).
In the field of food processing, NDE/NDT techniques can offer indisputable
advantages for the assessment of the food quality; Fig. 9.8 shows an interesting
application of the microwave techniques for the noninvasive monitoring of the
aging of wheel of cheese. In particular, the wheels of cheese are illumined with
low-power electromagnetic wave and the field scattered by the wheel of cheese is
measured with suitable antenna probes. The measured scattered field is then postprocessed with suitable inversion algorithms, and the distribution of the materials inside the wheel of cheese is identified. With this technique, it is possible to
easily detect unwanted materials such as air bubbles, molds, and mushrooms;
moreover, it is possible to assess the aging process monitoring the humidity inside
the cheese, since variations of humidity strongly change the electric characteristics of cheese. It is worth noticing that standard techniques make use of mechanical probes introduced inside the wheel of cheese. These procedures can introduce
inside the wheel of cheese bacteria or other pathogenic microorganism. The previous example concerns cheese; however, NDE/NDT techniques can be easily
extended for the monitoring of different typologies of expensive foods during the
production storing and distribution chain, and they offer great advantages with
respect to standard monitoring techniques.
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Conclusions
In this chapter, different innovative microwave techniques for foods and agriculture have been proposed. In particular, starting from innovative microwave imaging techniques commonly adopted for biomedical and industrial applications, the
chapter analyzed the possible applications of innovative microwave approaches
such as NDE/NDT and microwave imaging techniques for food and agriculture.
In this chapter, it has been demonstrated that particular measurement techniques
aimed at identity anomalies and crack in biological structures or industrial products can be easily adapted to food and agriculture applications; in particular, these
techniques can be successfully adopted to identify anomalies and deterioration of
the food characteristics. Microwave imaging techniques commonly used for biomedical applications (such as breast cancer detection) can be successfully adopted
to identify molds, mushrooms, and pathogen agents. The compactness and versatility of modulated scattering sensors make them particularly suitable for monitoring the industrial production chain, because they do not require power supply and
a direct connection with the measurement system. In conclusion, in this chapter, it
has been shown that modern microwave technologies could provide indisputable
advantages for industrial food processing. Microwave technologies can improve
the efficiency of the production, storing, and distribution chain, guaranteeing safe
and a real-time monitoring of the products. In the future, a lot of work will be necessary to integrate these technologies into standard industrial production, storing,
and control quality chain, usually based on conventional or obsolete technologies.
References
Ahmad A (2005) Wireless and mobile data Networks. Wiley, New York
Benedetti M, Donelli M, Lesselier D, Massa A (2007a) A two-step inverse scattering procedure for the qualitative imaging of homogeneous cracks in known host media—preliminary
results. IEEE Antennas Wirel Propag Lett 6:623–626
Benedetti M, Donelli M, Massa A (2007b) Multicrack detection in two dimensional structures by
means of GA-based strategies. IEEE Trans Antennas Propag 55(1):205–215
Benedetti M, Donelli M, Martini A, Pastorino M, Rosani A, Massa A (2006) An innovative
microwave-imaging technique for nondestructive evaluation: applications to civil structures
monitoring and biological bodies inspection. IEEE Trans Instrum Meas 55(6):1878–1884
Benedetti M, Donelli M, Franceschini G, Pastorino M, Massa A (2005) Effective exploitation
of the a priori information through a microwave imaging procedure based on the SMW for
NDE/NDT applications. IEEE Trans Geosci Remote Sens 43(11):2584–2592
Bengtsson NE, Ohisson T (1974) Microwave heating in the food industry. Proc IEEE 62(1):44–55
Bensky A (2004) Short-range wireless communication, fundamentals of RF system design and
application. Elsevier, New York
Bolomey JC, Gardiol G (2001) Engineering applications of the modulated scattering technique,
Artech House, London
Bolomey JC, Capdevila S, Jofre L, Tedjini S (2011) Sensitivity analysis for wireless dielectric reflectometry with modulated scatterers. In: Proceedings of 15th international symposium on antenna
technology application of electromagnetic Canadian radio science meeting ANTEM/URSI, pp 1–4
9
Microwave Imaging
189
Bolomey JC (1995) Frontiers in industrial process tomography. Engineering Foundation, NC
Bolomey JC (1996) Some aspects related to the transfer of microwave sensing technology. Proc
Mat Res Soc Symp 430:53–58
Bolomey JC, Joachimowicz N (1994) Dielectric metrology via microwave tomography: present
and future. Proc Mat Res Soc Symp 347:259–268
Bort E, Donelli M, Martini A, Massa A (2005) An adaptive weighting strategy for microwave
imaging problems. IEEE Trans Antennas Propag Lett 53(5):1858–1862
Caorsi S, Massa A, Pastorino M, Donelli M (2004a) Improved microwave imaging procedure
for non-destructive evaluations of two-dimensional structures. IEEE Trans Antennas Propag
52(6):1386–1397
Caorsi S, Donelli M, Massa A (2004b) Analysis of the stability and robustness of the iterative
multi-scaling approach for microwave imaging applications. Radio Sci 39(5):RS5008
Caorsi S, Donelli M, Franceschini D, Massa A (2003) A new methodology based on an iterative
multi-scaling for microwave imaging. IEEE Trans Microw Theory Tech 51(4):1162–1173
Caorsi S, Donelli M, Lommi A, Massa A (2004c) Location and imaging of two-dimensional scatterers by using a Particle Swarm algorithm. J Electromagnet Waves Appl 18(4):481–494
Caorsi S, Donelli M, Franceschini D, Massa A (2002) An iterative multiresolution approach for
microwave imaging applications. Microw Opt Tech Lett 32(5):352–356
Choi JH, Moon JI, Park SO (2004) Measurement of the modulated scattering microwave fields
using dual-phase lock-in amplifier. IEEE Antennas Wireless Propag Lett 3:340–343
Donelli M, Massa A, Pastorino M, Randazzo A, Rosani A (2005a) Microwave imaging for
nondestructive evaluation of civil structures. Insight: Non-destr Testing Condition Monit
47(1):1761–1776
Donelli M, Franceschini D (2010) Experiments with a modulated scattering system for throughwall identification. IEEE Antennas Wirel Propag Lett 9:20–23
Donelli M, Franceschini D, Massa A, Pastorino M, Zanetti A (2005b) Multi-Resolution iterative
inversion of real inhomogeneous targets. In-verse Prob 21:51–63
Donelli M, Massa A (2005) Computational approach based on a particle swarm optimizer for
microwave imaging of two-dimensional dielectric scatterers. IEEE Trans Microw Theory
Tech 53(5):1761–1776
Donelli M, Franceschini D, Franceschini G, Massa A (2005c) Effective exploitation of multiview data through the iterative multi-scaling method—an experimental assessment. Prog
Electromagn Res 54:137–154
Donelli M, Franceschini D, Rocca P, Massa A (2009) Three-dimensional microwave imaging
problems solved through an efficient multiscaling particle swarm optimization. IEEE Trans
Geosci Remote Sens 47(5):1467–1481
Donelli M, Pastorino M, Caorsi S (2001) A passive antenna system for data acquisition in scattering applications. IEEE Antennas Wirel Propag Lett 1:203–206
Dunaeva T, Manturow A (2010) The phenomenological model microwave drying kinetics of food
products. In: International Kharkov symposium on physics and engineering of microwaves,
millimeter and submillimeter waves (MSMW), p 1–3
Elber BR (2004) The satellite communication applications handbook. Artec House, London
Favro LD (2001) Thermosonic imaging for NDE, In: Thompson DO, Chimenti DE (eds)
Review of progress in quantitative nondestructive evaluation. American Institute of Physics,
Washington, DC, vol 20 A, p 478–482
Goldsmith A (2005) Wireless communications. Cambridge University Press, Cambridge
Gupta KC (1980) Microwaves. Wiley, New York
Guven G (2006) The innovation process of the microwave heat technology.In: IEEE conference
on technology management for the global future, PICMET-2006, vol 2, pp 788–793
Hall J, Dietrich F, Logan C, Schmid G (1999) Development of high-energy neutron imaging for
use in NDE applications, In: Green RE (ed) Nondestructive characterization of materials.
Elsevier Science, The Netherlands, vol IX, pp 693–698
Huang T, Mohan AS (2007) A microparticle swarm optimizer for the reconstruction of microwave images. IEEE Trans Antennas Propag 55(3 I):568–576
190
M. Donelli
King RJ, Stiles P (1984) Microwave nondestructive evaluation of composites. In: King RJ (ed)
Review of progress in quantitative nondestructive evaluation. Plenum, New York, vol. 3, pp
1073–1081
Kolawole MO (2002) Satellite communication engineering. Marcel Dekker, New York
Lacomme P, Hardange JP, Marchais JC, Normant E (2001) Air and space borne radar systems: an
introduction. William Andrew Publishing, New York
Leher C, Liedtke CE (1999) 3D reconstruction of volume defects from few X-ray images. In:
Leher C (ed) Computer analysis of images and patterns. Springer, Berlin, pp 257–284
Levanon N, Mozeson E (2004) Radar Signals. Wiley, New York
Liang W, Hygate G, Nye JF, Gentle DG, Cook RJ (1997) A probe for making near-field measurements with minimal disturbance: the optically modulated scatterer. IEEE Trans Antennas
Propag 1:772–780
Massa A, Franceschini D, Franceschini G, Pastorino M, Raffetto M, Donelli M (2005) Parallel
GA-based approach for microwave imaging applications. IEEE Trans Antennas Propag
53(10):3118–3127
Metaxas AC (1991) Microwave heating. Power Energy J 5(5):237–247
Morinaga N, Kohno R, Sampei S (2002) Wireless communication technologies. Kluwer
Academic Publisher, New York
Norton S, Bowler J (1993) Theory of eddy current inversion. J Appl Phys 73:501–512
Nyfors E (2000) Industrial microwave sensors—a review. Subsurf Sens Technol Appl 1:23–43
Ostradahimi M, Mojabi P, Noghanian S, Shafai L, Pistorius S, Lovetri J (2012) A novel
tomography system based on the scattering probe technique. IEEE Trans Instrum Meas
62(2):379–390
Pozar M (2011) Microwave engineering, 4th edn. Wiley, New York
Risman PO, Celuch-Marcysiack M (2000) Electromagnetic modeling for microwave heating
applications. In: 13th international conference on microwave, radar and wireless communications, MIKOM, vol 3, pp 167–182
Roddy D (2001) Satellite communications. McGraw Hill, New York
Rose JL, Pelts SP, Zhao X (2001) Defect characterization using SH guided waves. Rev Prog
Quant Nondestr Eval 20 A:142–148
Scott AW (1993) Understanding microwaves. Wiley, New York
Sisodia ML, Gupta VL (2004) Microwaves: introduction to circuits, devices and antennas. New
Age International, New Deli
Skolnik MI (1990) Radar handbook, 2nd edn. Mc Graw Hill, New York
Tehran HM, Laurin J, Kashyap R (2010) Optically modulated probe for precision near-field
measurements. IEEE Trans Instrum Meas 59(10):2755–2762
Thompson AR, Moran JM, Swenson GW (2004) Interferometry and synthesis in radio astronomy, 2nd edn. Wiley, Weinheim
Tirawanichakul S, Saenaratana N, Boonyakiat P, Tirawanichakul Y (2011) Microwave and hot air
drying of cashew nut: Drying kinetics and quality aspects. In: IEEE conference on humanities, science and engineering (CHUSER), pp 825–830
Varith J, Noochuay C, Netsawang P, Hirunstitporn B, Jamin S, Krairiksh M (2007) Design of
multimode-circular microwave cavity for agrifood processing.In: IEEE proceedings of AsiaPacific microwave conference, APCM, pp 1–4
Vauchamp S, Lalande M, Andrieu J, Jecko B, Lasserre JL, Pcastain L, Cadilhon B (2010)
Utilization of target scattering to measure high-level electromagnetic field: the MICHELSON
method. IEEE Trans Instrum Meas 59(9):2405–2413
Wilson TL, Rohlfs K, Huttemeister S (2009) Tools of radio astronomy, 5th edn. Springer, Berlin
Zoughi R (2000) Microwave nondestructive testing and evaluation. Kluwer Academic Publishers,
Dordrecht
Chapter 10
Radio Frequency Imaging
Gabriel Thomas and A. Manickavasagan
Introduction
As the radio spectrum in this particular range, from 3 kHz to 300 GHz, is being
used by a variety of devices, garage openers, multiple computer-related products
such as wireless routers, keyboards, and so on, one can expect that the possibilities
for designing and implementing a radio-based system for applications in food and
agriculture are quite good thinking about the availability of electronic transmitters
and receivers as well as possible antennas that are already in used on a myriad of
wireless commercial products. Nevertheless, in this chapter, a case for ultrasound
technology is made; pros and cons regarding this system are discussed.
Imaging Theory and Practical Considerations
In general terms, a good image should have excellent resolution and good contrast.
As propagating waves are used for subsurface imaging, resolution ΔR along the
line of sight of a transmitting and receiving sensor is given by a simple equation:
∆R = υ/2β
(10.1)
where υ is the propagation speed of the medium and β is the frequency bandwidth
of the transmitted wave. If only one sensor is used, this is referred to as a monostatic
G. Thomas (*)
Department of Electrical and Computer Engineering, University of Manitoba,
Winnipeg, MB, R3T 5V6 Canada
e-mail: thomas@ee.umanitoba.ca
A. Manickavasagan
Department of Soils, Water and Agricultural Engineering, College of Agricultural
and Marine Sciences, Sultan Qaboos University, PO Box 34, Al Khoudh, PC 123, Oman
A. Manickavasagan and H. Jayasuriya (eds.), Imaging with Electromagnetic Spectrum,
DOI: 10.1007/978-3-642-54888-8_10, © Springer-Verlag Berlin Heidelberg 2014
191
192
Fig. 10.1 Scenario where an
ultrasound sensor is used to
image the three holes shown
at the bottom of the picture
G. Thomas and A. Manickavasagan
P2 (w)
w
P1(w)
w
p2 (t)
p1 (t)
t
t
system and will constitute one of the simplest systems. To illustrate how frequency
bandwidth plays such an important role on defining resolution in this direction,
Fig. 10.1 shows a scenario where a rectangular pulse p(t) = rect(t/T) of duration T
seconds is sent to illuminate three closely spaced targets. As it is seen in the figure, the
Fourier transform of a rectangular pulse is a sinc function in frequency (Lathi 2005):
p(t) ↔ P(w) = T sinc(wT /2)
(10.2)
If broadly speaking and keeping this example as simple as possible, we define the
bandwidth of this rectangular pulse as the width of the main lobe in the frequency
plot (highest peak), the zero crossings occurred at w = 2π/T, and β is twice this
value. Thus, the shorter T is, the larger the bandwidth β and vice versa. Note how
p1(t) as being narrower than p2(t) has better chances to discern between the closely
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Fig. 10.2 Ultrasound image
at a 10 MHz, b 4 MHz of the
setup depicted in Fig. 10.1
spaced holes in the material shown in the picture. Also note that this means that
the main lobe of P1(w) is broader than the one for P2(w).
The sensor in Fig. 10.1 is a piezoelectric ultrasound transducer rated at
10 MHz. This operational frequency corresponds to the radio frequency (RF)
range as indicated in Table 1.1 in Chap. 1. Based on the discussion on how important the frequency bandwidth is in terms of resolution, one may think that microwave imaging can be a better alternative. This is actually not necessarily true,
because this high-resolution story has two parts: the bandwidth and the propagation speed in the medium. Consider the propagation speed in air for a microwave
system which is the speed of light, approx. 300,000,000 m/s, and compare it to the
speed of sound in water, approx. 1,500 m/s. To achieve a 1-mm resolution in such
scenarios, the microwave system needs to operate at 300 GHz and the ultrasound
system needs to operate at 750 MHz only. For food inspection, this propagation
speed will differ and usually will be less than the speed of light and water. For
electromagnetic signals, this speed depends on the material dielectric constant of
the medium, and a good list for food materials can be found in Ryynänen (1995).
The important thing to mention is that food and packaging materials may offer
better resolution because of lower propagation speeds. Take for example, the propagation speed of King Edward potatoes reported as 700–850 m/s in Povey (1989)
for ultrasound waves. One final thing about the quest for higher operational frequencies, unfortunately, attenuation is greater at higher frequencies (Chanamai
and McClements 1998; Trabelsi and Nelson 2003), thus, for example, one cannot
go and select the highest possible frequencies offered by a radar- or ultrasoundbased equipment without realizing that at one point the wave will not be able to
penetrate much of the material.
Having discussed resolution in the direction along the line of sight of the sensor, the cross-range or lateral resolution will be discussed next. Figure 10.2 shows
the ultrasound image of the scenario depicted in Fig. 10.1. Note how operating at
higher frequencies did offer better range resolution but in the cross-range direction the holes appear blurred. The images were obtained by just simply moving
194
G. Thomas and A. Manickavasagan
Fig. 10.3 Illustration of the
acoustic power emitted by a
non-focused transmitter
Fig. 10.4 Left unfocused
image as collected in A-scan
mode. Right focusing
obtained as proposed in Li et
al. (2005) and Lazaro et al.
(2009)
the sensor and collecting the ultrasound pulses to form a B-scan image. The
spreading of the acoustic returns in the lateral direction is due to the spreading
of the acoustic beam. Think of something as simple as a water hose, the water
trajectory as it is coming out of the hose does not follow a nice concentrated
beam, and the same can be said for the acoustic power emitted by an ultrasound
transducer as illustrated in Fig. 10.3. The main lobe angle Ψ follows the relation
sin(Ψ/2) = 1.2(υ/Dw), where D is the diameter of the transducer, υ the propagation speed mentioned before, and w is the operational frequency. This beam
spreading is also found in an antenna.
Inverse scattering methods can be implemented to focus an image and alleviate the problem of this acoustic signature spreading at the expense of computational time. An alternative method would be space–time beamforming which relies
mainly on time shifts and summations of the collected signals which makes the
image reconstruction fast (Li et al. 2005; Lazaro et al. 2009). Figure 10.4 shows
how the signatures from three simulated point targets can be focused by these
techniques. Another technique for focusing is based on synthetic aperture imaging,
as done in radar applications (Soumekh 1999) and ultrasound imaging (Ylitalo and
Ermert 1994). The basic concept is that the direction of wave propagation determines the corresponding angular frequency of the returned signal as depicted in
Fig. 10.5. Without trying to explain each of these methods in detail, let us summarize two important characteristics between them: (1) speed of reconstruction and
(2) image quality, with a simple graphic as shown in Fig. 10.6. By no means are
we implying that the relationship is linear, but in general terms, the tendency follows intuition, as the method is more complex, such as solving differential equations for inverse scattering to something as simple as B-scan imaging that requires
only displaying the different collected scans, image quality will benefit from taking more computations during the image reconstruction. The references at the
end of the chapter offer a good list of publications that explain details on these
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195
scan direction
target
acoustic beam
returned
signal
Fig. 10.5 Depiction of the change in frequency of the returned signal that contains information
for focusing the returns. Also evident on this figure is how the widening of the beam causes to
receive an echo even when the sensor is located further to the right of the target
Image quality
inverse scattering
synthetic aperture
focusing
beam
forming
A-Scan
Computational speed
Fig. 10.6 The more mathematical complexity of the modeling of the different imaging methods
tends to reconstruct images with better quality at the expense of computational time
methods. To complicate things a bit, the lingo varies depending on the application, even the names of the different techniques. For example, synthetic aperture
focusing can also be known as frequency-wavenumber migration (Gilmore et al.
2006) and holographic imaging (Ylitalo et al. 1989), and beamforming can also be
known as confocal imaging (Fear et al. 2002).
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G. Thomas and A. Manickavasagan
Fig. 10.7 Block diagram of
a flaw detector
Instrumentation and Transducers Used for Imaging
Ultrasound test equipment can be classified in a number of different ways; this
may include portable or stationary, contact or immersion, manual or automated.
One of the first steps to take place before any measurement will be to install a
probe: single-transducer receives and transmits, or dual—two transducers are
used for the same receiving and transmitting purposes. Figure 10.7 shows a general block diagram of what a typical ultrasound flaw detector consists on. We can
see how the pulser/receiver constitutes a major part. Ultrasonic pulser receivers
are well suited to general purpose ultrasonic testing. Along with appropriate transducers and an oscilloscope, they can be used for detection and thickness gauging.
Ultrasonic pulser receivers provide a unique, low-cost ultrasonic measurement
capability.
The pulser section of the instrument generates short, large amplitude electric
pulses of controlled energy, which are converted into short ultrasonic pulses when
applied to an ultrasonic transducer. Most pulser sections have very low impedance
outputs to better drive transducers. Control functions associated with the pulser
circuit include the following:
• Pulse length or damping (The amount of time the pulse is applied to the
transducer.)
• Pulse energy (the voltage applied to the transducer. Typical pulser circuits will
apply from 100 to 800 V to a transducer.)
In the receiver section, the voltage signals produced by the transducer, which represent the received ultrasonic pulses, are amplified. The amplified RF signal is
available as an output for display or capture for signal processing. Control functions associated with the receiver circuit include the following:
• Signal rectification (The RF signal can be viewed as positive half wave, negative half wave, or full wave.)
• Filtering to shape and smooth return signals
• Gain, or signal amplification
• Reject control
Digital technology has allowed the implementation of ultrasound detectors in
compact packages that can be connected to a computer via for example a USB
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Fig. 10.8 Digital ultrasound
flaw detector
Fig. 10.9 Block diagram of
a digital-based ultrasound
flaw detector
port such as the US Key flaw detector by Lecoeur Electronique (2013) shown in
Fig. 10.8.
Examples of digital ultrasound detector designs can be found in Song et al.
(2007), Liao and Xi (2009). Furthermore, with today’s smart mobile telephones,
one can eliminate the computer and develop very portable systems such as the one
proposed in Richard et al. (2011). Figure 10.9 shows a block diagram depicting
the general implementation of these digital devices.
With a typical commercial non-focused ultrasound transducer, the beam can
be assumed to follow a straight path in the close vicinity from the surface of
the transducer. Usually, the strongest reflections come from the area directly in
front of the center of the transducer except from some null regions as shown in
Fig. 10.10 close to the transducer. This region close to the transducer is referred
to as the near-field area also known as Fresnel zone (Olympus NDT 2006), and
afterward, the spreading of the beam is more prominent, and thus, small reflections can arrive from targets located away from the line of sight, even at distances
wider than the element width of the transducer, i.e., the diameter at the bottom of
the circular transducer. Figure 10.10 shows a diagram of the near- and far-field
G. Thomas and A. Manickavasagan
198
(a)
near field dnf
far field dff
(Fresnel Zone)
(Fraunhofer Zone)
Acoustic pressure
(b) 1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
Distance x in mm
400
500
600
Fig. 10.10 a Depiction of the near- and far-field regions. Different gray levels within the beam
depict different acoustic energy levels. Higher energy is shown in a darker gray-level value. b
Acoustic pressure amplitude p0, at a distance x along the line of sight of the transducer, in a
homogeneous medium from the
of radius
source a = D/2 (Blitz and
Simpson 1996). Model fol1/2
− x where k = w / υ. Far fields
lows the expression p0 (x) = pmax sin (k/2) x 2 + a2
does not have the nulls as in the near-field region
zone (also known as Fraunhofer zone). The near-field distance is given by Blitz
and Simpson (1996):
dnf = D2 w/4υ
(10.3)
Note how the same parameters that affect the main lobe spreading angle also
define these operational zones. Similar effects are found in antennas (Hansen
1985). These zones then would indicate that to increase the contrast of a point target with respect to the medium, one has to be careful when operating in the near
zone because the transmitted acoustic energy can be so weak to cause a considerable reduction in contrast. Furthermore, there is always an initial pulse response
from the contact surface between the transducer face and the propagating medium.
This is shown in Fig. 10.11, where acoustic energy at the location right where the
transducer face contacts the material under inspection, a metal bar, is shown as
high amplitude peaks in the first 30 samples. Two holes drilled on the metal bar
are shown in the middle and the last strong returns at the end of the scan corresponding to the other side of the bar as the wave hits it. The small amplitude corresponding to the holes is mainly due to the small cross section of these targets.
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100
Amplitude
50
0
-50
-100
0
20
40
60
80
100
120
140
160
180
200
Time Index
Fig. 10.11 Strong responses from the contact surface shown at the beginning of the scan can
obscure small amplitude signatures from targets such as the ones located around time indexes 90
and 120
Note how if the holes were located too close to the contact surface of the transducer, there is the possibility of those signatures to be buried within the return of
this initial pulse.
Besides the cross section of the targets, another reason why targets can return
a small energy acoustic signature is due to the acoustical impedance. For the case
in Fig. 10.11, the holes contain air and high-frequency ultrasound waves cannot
travel though this medium, and the acoustic wave is reflected back to the sensor, mentioning again that the weak amplitudes are due to the surface area of the
holes. One important factor is that if the first medium is, for example, water with
an acoustic impedance Rw and the second medium is a food product with acoustic
impedance Rf, the ratio R of the amplitude of reflected wave to the incident wave
is given by Awad et al. (2012):
R = (Rw − Rf )/(Rw + Rf )
(10.4)
Thus, maximum penetration of the acoustic wave is obtained if both impedances
are matched. Better acoustic matching would have reduced the initial peaks mentioned in Fig. 10.11. If the objective is to receive a pulse from a foreign object
within the inspected area, then a mismatched is required; otherwise the foreign
object will be invisible or appearing with very low contrast in the image. This
acoustic impedance mismatch and successful detection of foreign objects in the
case of cheese and marmalade with foreign objects consisting on fragments of
bone, steel, and wood were reported in Haegstrom and Luukkala (2001).
In order to improve contrast, one solution can be focusing the acoustic beam.
Commercial transducers can be focused so that the sound energy concentrates in a
more confined area in a cylindrical or spherical way as shown in Fig. 10.12. This
increases the sensitivity of the system at only one specific small region located at a
fixed distance from the transducer.
This impedance matching and impedance difference between the propagating
medium and the target as well as the focusing capabilities of the sensor to increase
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G. Thomas and A. Manickavasagan
Fig. 10.12 Different
focusing: a cylindrical, b
spherical
contrast also apply to the microwave case. To summarize what has been said
regarding these two types of systems, the following considerations can be given:
• The transmitted wave requires a propagation medium. At the microwave level,
usually, an antenna is involved and air would be the medium in most cases,
although some coupling medium in the form of liquids such as glycerin, soybean
oil, and alcohol have been used for breast imaging applications (Salvador and
Vecchi 2009). For the ultrasound case, popular commercial available transducers
must use a coupling medium such as ultrasound gel or water although there are
some contact transducers that require no coupling (Blomme et al. 2002). Based
on the food product or processing aspect where this technology is intended to
be used, this would be one of the first things to consider. For example, a contact
transducer may be unfeasible to use in an automated system since good contact
may require an operator to verify good pressure of the transducer to the material.
There is really nothing to say about trying to inspect cookies in water.
• Have an idea of the impedance mismatch and attenuation of the materials to be
tested; this would not only allow the coupling but also the feasibility of detecting for example foreign objects.
• Keep in mind the operational frequencies; this affects the resolution, attenuation, and spreading of the beam. Higher frequencies come with a trade-off, more
attenuation but a more concentrated beam, and higher resolution.
• Image reconstruction techniques would take computational time, from few seconds to possible hours. If small-diameter transducers are to be used, they may
allow enough lateral resolution to obtain high-quality B-scan images fast. A new
type of ultrasound sensors, capacitive micromachined ultrasonic transducers
(Emadi et al. 2012), has proven to be flexible enough to incorporate them into
small-dimensional silicon-based sensors.
Finally, if only detection is to be considered, a low-frequency transducer may
be a good option. As the use of low frequencies offers less absorption in air, this
will then tend to compensate for the impedance mismatch problems. Even though
some materials have been investigated as matching layers, these by themselves
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Fig. 10.13 Image of a 5-mm
hazelnut fragment within a
chocolate sample
have a very high attenuation coefficient (Trabelsi and Nelson 2003; Li et al. 2005).
For example, a piezo ultrasonic air transducer rated at 25 kHz (Steminc 2013)
mainly used for distance measurements costs only 15 dollars but offers virtually
no possibilities of operating at other frequencies. Nevertheless, the implementation
of a system consisting of generating a burst wave modulated at this frequency or a
continuous sinusoidal wave will reduce even more the total costs of such a system.
Imaging Applications in Agricultural and Food Production
Regarding capacitive micromachined ultrasonic transducers, they have successfully been used recently on inspection of food products as air couple devices
working at less than 200 KHz. As it can be seen in Fig. 10.13 (Pallav et al. 2009),
high-resolution images are obtained by taking advantage of the relatively large
bandwidth offer by these new transducers and then taking advantage of pulse compression techniques that require large bandwidth signals.
New technologies at very high frequencies, 210 GHz, have been developed. By
obtaining transmitted beams with lateral resolution of only few millimeters, Ok et
al. (2013), reported that excellent images of crickets buried in flour as shown in
Fig. 10.14. Based on these results, this is another new area to be considered.
In order to include a unique electromagnetic RF hybrid method in which food
can be heated using this type of waves, in combination with a thermal infrared
camera, an example is shown in Fig. 10.15. Here, the temperature of walnut kernels differs as the kernels are opened or closed (Wang et al. 2006). This example
as well as the case for microwave imaging in another chapter of this book reinforces the fact that ultrasound is not necessarily a better solution, both techniques
offer different attributes to the food and agriculture industry.
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G. Thomas and A. Manickavasagan
Fig. 10.14 a Picture of the crickets buried within noodle flour shown in (b). c Images at different distances d from the sensor to the sample surface
Fig. 10.15 a Picture of a group of walnut kernels in which the ones numbers 1, 2, and 3 are
closed and 4, 5, and 6 are open. b Thermal imaging illustrating the differences of both cases after
heating the walnuts using a 27 MHz radio frequency
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Conclusions
Several aspects of ultrasound imaging were presented in this chapter. Rather than
proposing this technique as a better solution, an emphasis was made to distinguish the similarities and differences between using RF waves in an ultrasound
system or electromagnetic waves propagating in air. After all, imaging algorithms
such as synthetic focusing can actually be used in both systems. What can be said
for an ultrasound system is that it would be relatively inexpensive, that compact
hardware can be used and that the technology is already being proposed for food
inspection. Thus, such technology ought to be considered taking into consideration
that high-frequency sound waves are highly attenuated in air and in most materials, acoustic impedance mismatch must be accounted for otherwise no energy can
penetrate the surface of the material if air is the main propagating medium but at
the same time that mismatch must exist in order to receive a pulse that can be visualized as a foreign object for example.
Acknowledgements We thank The Research Council (TRC) of Sultanate of Oman for funding
this study (Project No. RC/AGR/SWAE/11/01—Development of Computer Vision Technology
for Quality Assessment of Dates in Oman). We greatly appreciate Dr. Gyeogsik Ok from the
Food Safety Research Group, Korea Food Research Institute, for permission of using the images
shown in Fig. 10.14. We also appreciate the permission given by Dr. David Hutchins, from the
University of Warwick in the UK, to use the image in Fig. 10.13. Also special thanks to Dr.
Juming Tang from Washington State University for the images in Fig. 10.15.
References
Awad TS, Moharram HA, Shaltout OE, Asker D, Youssef MM (2012) Applications of ultrasound
in analysis, processing and quality control of food: a review. Food Res Int 48(2):410–427
Blitz J, Simpson G (1996) Ultrasonic methods of nondestructive testing. Chapman & Hall,
London
Blomme E, Bulcaen D, Declercq F (2002) Air-coupled ultrasonic NDE: experiments in the frequency range 750 KHz–2 MHz. NDT E Int 35:417–426
Chanamai R, McClements DJ (1998) Ultrasonic attenuation of edible oils. J Am Oil Chem Soc
75(10):1447–1448
Emadi TA, Thomas G, Pistorius S, Buchanan DA (2012) Capacitive micromachined ultrasonic transducer array with pencil beam shape and wide range beam steering. Eurosensors,
Cracow, Poland
Fear EC, Li X, Hagness SC, Stuchly MA (2002) Confocal microwave imaging for breast
cancer detection: localization of tumors in three dimensions. IEEE Trans Biomed Eng
49(8):812–822
Gilmore C, Jeffrey I, LoVetri J (2006) Derivation and comparison of SAR and frequency-wavenumber migration within a common inverse scalar wave problem formulation. IEEE Trans
Geosci Remote Sens 44(6):1454–1461
Haegstrom E, Luukkala M (2001) Ultrasound detection and identification of foreign bodies in
food products. Food Control 12(1):37–45
Hansen RC (1985) Focal region characteristics of focused array antennas. IEEE Trans Antennas
Propag 33(12):1328–1337
Lathi BP (2005) Linear systems and signals, 2nd edn edn. Oxford University Press, Oxford
204
G. Thomas and A. Manickavasagan
Lazaro A, Girbau D, Villarino R (2009) Simulated and experimental investigation of microwave
imaging using UWB. Prog Electromagnet Res 94:263–280
Lecoeur-electronique (2013) http://www.lecoeur-electronique.com/. Accessed on 26 Nov 2013
Li X, Bond EJ, Van–Veen BD, Hagness SC (2005) An overview of ultra-wideband microwave
imaging via space-time beamforming for early-stage breast-cancer detection. IEEE Antennas
Propag Mag 47(1):19–34
Liao G, Xi S (2009) Intelligent embedded portable of ultrasonic testing device. In: International
workshop on intelligent systems and applications 2009. ISA 2009, pp 1–4, 23–24
Ok G, Choi SW, Park KH, Chun HS (2013) Foreign object detection by Sub-Terahertz QuasiBessel beam imaging. Sensors, vol 13
Olympus NDT (2006) Ultrasonic transducers technical notes. Accessed on March 2006
Pallav P, Hutchins DA, Gan TH (2009) Air-coupled ultrasonic evaluation of food materials.
Ultrasonics 49(2):244–253
Povey MJW (1989) Ultrasonics in food engineering Part II: applications. J Food Eng 9(1):1–20
Richard WD, Zar DM, Chutani S, Solek R (2011) FDA-approved smartphone ultrasound system.
International symposium on ultrasonic imaging and tissue characterization, Arlington, VA
Ryynänen S (1995) The electromagnetic properties of food materials: a review of the basic principles. J Food Eng 26(4):409–429
Salvador SM, Vecchi G (2009) Experimental tests of microwave breast cancer detection on phantoms. IEEE Trans Antennas Propag 57(6):1705–1712
Song Z, Wang Q, Du X, Wang Y (2007) A high speed digital ultrasonic flaw detector based on
PC and USB. In: Instrumentation and measurement technology conference proceedings,
IMTC 2007. IEEE, pp 1–4, 1–3
Soumekh M (1999) Synthetic aperture radar. Signal processing with MATLAB algorithms.
Wiley, New York
Steminc (2013) www.steminc.com/PZT/en/piezo-ultrasonic-air-transducer-25-khz. Accessed on
2013
Trabelsi S, Nelson SO (2003) Free-space measurement of dielectric properties of cereal grain and
oilseed at microwave frequencies. Measur Sci Technol 14(5):589
Wang S, Tang J, Sun T, Mitcham EJ, Koral T, Birla SL (2006) Considerations in design of commercial radio frequency treatments for postharvest pest control in inshell walnuts. J Food
Eng 77(2):304–312
Ylitalo J, Alasaarela E, Koivukangas J (1989) Ultrasound holographic B-scan imaging. IEEE
Trans Ultrason Ferroelect Freq Contr 36(3):376–383
Ylitalo JT, Ermert H (1994) Ultrasound synthetic aperture imaging: monostatic approach. IEEE
Trans Ultrason Ferroelec Freq Contr 41(3):333–339