Study on the Meat Quality Detection Techniques
FU Yan, GUO Pei-yuan, SUN Mei, LIN Yan
XU Ran-ran
School of Computer Science and Information Engineering
Beijing Technology and Business University
Beijing, China
e-mail address :ggppyy@126.com
Zolix Instruments Co., Ltd
Beijing, China
Abstract—This paper describes AOS and ATS detection
techniques, computer vision detection technology, NIR detection
technology, hyperspectral
image detection technology.
Hyperspectral image detection technology for meat quality
detection has become a research hotspot. The basic principle of
hyperspectral image detection technology and the research status
in meat quality detection using Hyperspectral image at home and
abroad are introduced in the paper. The prospect of future
research in meat quality detection using Hyperspectral image
detection technology is proposed to researchers on the related
study as a reference.
Keywords-meat quality; nondestructive detection technique;
Hyperspectral image detection technology
1. INTRODUCTION
In recent years, with the rapid growth of consumption of
livestock meat and meat products, people put forward the
higher request of meat quality. For the meat, mo
st consumers are concerned about the meat quality and price.
It makes demands of meat processing industry, meat safety,
edible quality evaluation, identification of meat authenticity
and adulteration and so on[1]. The quality of meat and its
products is related to the most people's health problems.
Therefore, if can rapidly and accurately identify meat quality,
it becomes a serious problem that should be solved , in order
to reduce the risk of eating the meat which is containing
clenbuterol, heavy metals, microorganisms, water and other
issues.
The meat organoleptic quality usually measure from
general color, tenderness , flavor of the meat , water retention
and so on. More traditional detection methods is detrimental
testing , such as organoleptic detection, physical and chemical
detection, microbiological chemical testing and so on. At
present, the meat quality detection technology research has
had certain achievement in the scope of the world, such as
meat chemical composition analysis, the determination
technology of the freshness of meat, using image processing
technologies for livestock body classification and meat quality
detection, and has developed into instrument equipment in
practical production. Especially Dane and German, who have
developed the production line which can detect meat quality
online based on near infrared spectroscopy technology
(NIR)[2]. This article describes various methods of meat
quality nondestructive testing in the modern testing field,
including artificial olfactory and artificial taste detection
technology, computer vision detection, NIR detection,
This project was supported by Beijing Municipal Natural
Science Foundation (4092012).
The authors are FU Yan, Master,GUO Pei-yuan, Professor, SUN Mei,
Associate Professor, and LIN Yan, Master, School of Computer Science and
Information Engineering, Beijing Technology and Business University,
Beijing, China; Xu Ran-ran, engineer, XU Ran-ran, Zolix Instruments Co.,
Ltd, Beijing, China. Corresponding author: GUO Pei-yuan.
hyperspectral image testing technology. Hyperspectral image
testing technology used in nondestructive testing of meat
quality was highlighted.
2. MEAT QUALITY NONDESTRUCTIVE TESTING
TECHNOLOGY
2.1 Artificial Olfactory and Artificial Taste Detection
Technology
Artificial olfactory and artificial taste detection technology
is a new testing technology developed in recent years, which
can simulate smell and taste function of humans and animals.
Artificial olfactory and artificial taste, which are also called
electronic nose and electronic tongue, can be used to identify
the smell of food, meat authenticity and adulteration, in order
to control the entire production process technology and ensure
the product quality.
When the pork and the role of various microbial
contamination or corruption caused by the decomposition of
the enzyme and its sour nature of the fermentation will
produce sulfide and ammonia.Therefore, we can use artificial
olfactory system testing the variation of the time of sulfide and
ammonia concentrations, to determine the freshness of meat.
Currently, the study which use artificial olfactory system to
test the freshness of meat is quite extensive abroad. Domestic
experts have begun to apply artificial olfactory system
detecting the freshness of meat. For example, Guo Peiyuan
and Qu Shihai[3] build a set of intelligent detection recognition
system based on electronic information technology, optical
detection technology, image processing and neural network
pattern recognition technology. It collected ammonia and
hydrogen sulfide released from the process of pork corruption,
and collected data as a set of neural network input. It is a
reliable basis for determing the freshness of pork[4]. Olfaction
visualization technology is currently a new branch of artificial
olfactory research. It is able to solve some common problems
in gas sensors, and compensate for certain deficiencies in the
electronic nose system. Its outstanding feature is translating
smell information into visual information, so that smell is
"visible." Olfaction visualization technology was first
proposed by Professor Kenneth S. Suslick from University of
Illinois at Urbana - Champaign. It is a visualization methods
for qualitative and quantitative analysis based on colors
changing of gas to be detected after reaction with chemical
reagent[5].
Artificial taste system (ATS) technology started not long,
the technology is not mature, yet to be further developed. The
combination of artificial olfactory and artificial taste
technology can further improve the detection and recognition
of food, but the related research is in its infancy.
chemical values to make model, low accuracy of quantitative
analysis, complicated calculation model established and so on.
2.2 Computer Vision Technology
Computer vision testing technology is a practical
technology. It obtains the object image with the image sensor
(commonly used high-resolution CCD), then convert the
image into digital image, understand and identify the image by
the criteria for computer simulation of the human, and make
the practical techniques of appropriate conclusions. Image
processing and image analysis is the core of computer vision
technology[6-7].
Most of the current nondestructive testing method used
only a single means of detection, more traditional signal
preprocessing techniques and pattern recognition methods.
They often only have better information response to one or
two indexes, but not evaluate comprehensive using a variety
of information. The overall quality of meat is complex, it
should be evaluate comprehensive by a number of indicators.
Therefore, organic integration of these detection techniques,
taking full advantage of multiple information, evaluating meat
simply, quickly, accurately and comprehensively is the focus
of future research and development.
Computer vision system has the advantages of
nondestructive ,reliable and fast, so it is widely used in meat
quality testing , not only in the visible area, but extends to the
near infrared, infrared and other areas, not only in the visible
area, but extends to the near infrared, infrared and other areas.
Zhang Zhe[8] detect the fat content of pig eye muscle
intramuscular with computer vision technology. It is also used
to study fluctuations and grading of kidney meat quality. For
example, we can detect a clear PSE kidney meat with the 400
~ 700nm optical reflection measurement system..
2.3 Near Infrared Spectroscopy Technology
Near infrared spectroscopy technology has been a new
optical detection technology which has been widely used
recently in the food industry. The technology integrated
spectroscopy,
chemometrics,
computers
and
other
multidisciplinary modern analytical techniques. It has the
advantage of rapid online analysis, nonpollution,
nondestructive analysis, enabling nondestructive testing,
enabling remote analysis and detection and so on. It is not
only able to detect the chemical composition of meat in the
traditional ways, but also involved in sensory quality
evaluation, species identification and other aspects of meat
quality. It has become a very active research area. Because of
that the majority of organic compounds containing different
hydrogen groups in meat, the content of these components can
be determined by near infrared spectroscopy. And through
further analysis can get more information related with the
meat quality[2]. There are many domestic and foreign experts
using near infrared technology for meat detection. For
example, it has been successfully achieved detecting the
water changes of meat in the heating process with near
infrared fiber optic probe. It provides a new way of effective
and reasonable control processing for meat industry[9].
Warnecke-H W studied the influences of additives to fat,
moisture and protein content during meat processing with near
infrared technology, found that most of the additives affect the
absorption of near infrared[10]. Hou Ruifeng and other people
in the domestic established a prediction model of total volatile
basic nitrogen (TVB-N), classification spectral data by cluster
analysis, enabled rapid nondestructive testing of meat[11].
However, near infrared spectroscopy also has its fatal
flaws, such as requiring a large representative sample of
3. HYPERSPECTRAL IMAGE DETECTION
TECHNOLOGY OF MEAT QUALITY
Hyperspectral image detection technology is being more
and more favour of researchers at home and abroad.
Spectroscopy technique can detect meat material structure,
composition and other internal quality information. Computer
vision technology can fully reflect the external characteristics
of meat. Therefore, hyperspectral images can reflect the
overall quality of meat.
3.1 Hyperspectral Image Detection Technology
As a new detection technology, hyperspectral image
technology focus optics, optoelectronics, electronics,
information processing, computer science and other fields of
advanced technology, which organic combined the traditional
two dimensional imaging techniques and spectroscopy. The
technology has a characteristic of super multiband, high
spectral resolution and one map. Therefore, hyperspectral
technology has a greater detection advantage and detection
accuracy in agricultural and livestock products, food quality
and safety testing[12]. Hyperspectral imaging technology has
been used in fruit internal quality, surface contamination and
bruises testing[13], vegetable maturity and internal quality
testing, and applied to the internal quality of the meat
inspection[14].
The hardware components of hyperspectral image
detection is including light source, CCD camera, computer
equipped with image acquisition card and monochromator.
The spectral range is 200-400nm, 400-1000nm, 900-1700 nm,
1000-2500 nm. Hyperspectral image detection system based
on image spectrometer is shown in Fig.1. It is mainly
composed of the matrix CCD camera and image spectrometer.
When it works, image spectrometer will divide the light of
reflect or transmit from test objects into monochromatic light
source, and then went into the CCD camera. The system uses
"push broom type" imaging method to get the hyperspectral
image, matrix CCD detector makes horizontal arrangement
completed horizontal scan (X direction) in the optical focal
plane of the vertical direction. Acquire the information of the
image of the test object with all wavelengths of each pixel in
strip space. At the same time, in the process of advancing
conveyor belt of detection system, the arrangement of detectors
sweep out a ribbon track to complete the vertical scan (Y
direction). Integrated vertical and horizontal scanning
information, three-dimensional hyperspectral image data of
samples can be got, as shown in Fig.2.
At home and abroad, there have been some related
research reports in hyperspectral image detection technology
of meat quality. For example, Jinjun Xia[15] studied scattering
properties of different beef in the near infrared spectral range.
They found that the value of beef tenderness and the scattering
of spectrum has a higher correlation. This indicates that the
use of spectral scattering properties can predict tenderness of
beef. Cluff[16] and other people predict beef tenderness with
hyperspectral scattering properties. They predicted the highest
correlation coefficient was 0.76. However, there is no related
research reports based on hyperspectral image detection
technology meat quality parameters of the test.
blue line is the position of the waveform of bacteria. As a
result, the study to detect fish plaque with hyperspectral image
detection technology is feasible and practical significance.
CCD
Grating
spectrometer
a.749nm
b.PC-6
Figure 3. The plaque of Hyperspectral principal component analysis
Imaging lens
Light source
Computer
Electricity
move stage
Sample
Figure 1. Hyperspectral imaging system conflduration
Figure 4. The position of plaque and no wavelength curve fitting
At the same time, they also studied the detection of pork
quality and related issues with hyperspectral image detection
technology. Hyperspectral images are got in the near infrared
range. Grayscale and 3D stereo false color picture of fresh
pork samples in near infrared range hyperspectral images
within a band is shown in Fig. 5. It can be seen from the
figure, the near infrared images are not very good. But a large
amount of reflectivity information can contribute to the
follow-up analysis.
Figure 2. 3d hyperspectral imaging data
Guo Peiyuan, Fu Yan and other people is studying the
relevant issues about fish quality testing with hyperspectral
image detection technology. Hyperspectral images are
obtained in the 400-1000nm wavelength range of 512 bands.
We can get the effective characteristic wavelength with
principal component analysis. For example, fish hyperspectral
images collected by the hyperspectral imaging system, which
was carried out by principal component analysis, can clearly
see some parasites in the sixth principal components band
image, which is shown in Fig.3. From the figure, we can
clearly identify the plaque location of hyperspectral images
after principal component analysis. Fig.4 shows the location of
nonplaque and plaque wavelength curve fitting graph. Red line
in this figure is the position of the waveform to nonbacterial,
a. Gray image
b. 3D stereo false color
Figure 5. Pork sample hyperspectral image detection gray image and 3 D
stereo false color
3.2 Outlook of Meat Quality Hyperspectral Image Detection
Technology
Hyperspectral image detection combines the advantages of
spectroscopy and imaging study. Testing the quality of meat
using hyperspectral image detection technology is rising in
recent years. This paper through a fish plaque detection, the
preliminary study shows that hyperspectral imaging
technology can visualize the analysis of meat quality.
Hyperspectral imagery for meat quality testing has good
results, researchability and feasibility.
Hyperspectral imaging system has obvious advantages in
determining the characteristics wavelength of the quality
parameters. Through researching and testing, we can
determined the most effective characteristics wavelength of
meat quality. Designed a number of wavelengths of spectral
imaging systems,and then applied to the actual production. In
this way, this can greatly improve the detection effectively, to
achieve the aim of online, fast and nondestructive testing.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
4. CONCLUSION
With the development of science and technology, meat
quality nondestructive testing technology will integrate a
variety of detection techniques direction. Due to hyperspectral
image detection technology can obtain extensive images and
spectral information of testing meat, it can be nondestructive
testing of meat quality and comprehensive evaluation, so it
has great potential for development in meat quality testing.
Light absorption characteristics of different meat are quite
different. The advantage of hyperspectral image detection is
that it can choose the best effective characteristic wavelength
according to the test target. This increases the potential
applications of hyperspectral image detection technology. To
extend the field of hyperspectral image detection technology
in meat quality testing , we can apply hyperspectral image
detection technology to do feasibility studies for other
important meat quality, such as freshness, drug residues,
heavy metals, processing aids, additives and so on.
Nondestructive testing technology of meat quality, especially
hyperspectral image detection technology in China is still in
the experimental research stage, and did not really put them
into actual production. Therefore, it has a very important
practical significance to the modern development of meat
industry in our country to speed up the market application of
meat quality nondestructive testing equipment, and achieve
meat online, fast, nondestructive testing.
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
Monin G, “Recent methods for predicting quality of whole meat,”
J.Meat Science. vol. 49(2), 1998, p. 231.
XU Xia, CHENG Fang, and YING Yi-bin, “Application and Recent
Development of Research on Near-Infrared Spectroscopy for Meat
Quality Evaluation,” J. Spectroscopy and Spectral Analysis. vol. 29(7),
2009, pp. 1876–1880.
XU Shi-hai and GUO Pei-yuan, “Study on detection method to fresh
degree of meat based on many message dealing,” J. Journal of Beijing
Technology and Business University(Natural Science Edition). vol.
24(5), 2006, pp.26–31.
XU Guan-nan, GUO Pei-yuan, and YUAN Fang, “Development of
Nondestructive Detection Techniques of Pork Freshness,” J. Journal of
Beijing Technology and Business University(Natural Science Edition).
vol. 28(1), 2010, pp.14–17.
ZOU Xiao-bo and ZHAO Jie-wen, “Nondestructive testing technology
of agricultural products and data analysis method,” M. Beijing: Light
Industry Press of China. pp. 1–8, 2008.
CHEN Chun, “Computer image processing technology and algorithm,”
M. Beijing: Tsinghua University Press. pp. 3–15, 2003.
LI Ming-jing, LIU Guan-yong, ZHANG Zhi-wei, “The computer vision
based beef automatic grading technology,” J. Meat Science. vol. 6, 2007,
pp. 18–20.
ZHANG Zhe, “The computer vision technology based the fat content
determination of pig eye flesh,” J. Swine Industry Science. vol. 2, 2006,
pp. 24–25.
SHI Su-jia, “The food analytical technology of no-damage by nearinfrared spectroscopy,” J. Food Research and Developent. vol. 3, 2007,
pp.177-179.
Warnecke H W, lehmann F, chwager K. “Effects of Additives on
Results of Near Infrared Analyses” J. Fleischwirtschaft. vol.52(4), pp.
196-200, April 1997.
HOU Rui-feng, HUANG Lan, and WANG Zhong-yi, “Preliminary study
on detecting freshness of meat with near-infrared spectrum,” J.
Spectroscopy and Spectral Analysis. vol. 26(12), pp. 2193–2196,
December 2006.
A A Gowen, C P Donnell, and PJ Cullen, “Hyperspectral imaging-an
emerging process analytical tool for food quality and safety control,” J.
Trends in Food Science&Technology. vol. 18, 2007, p. 590.
MA Ben-xue, YING Yi-bin, and RAO Xiu-qin, “Advance in
Nondestructive Detection of Fruit Internal Quality Based on
Hyperspectral Imaging,” J. Spectroscopy and Spectral Analysis. vol. 29,
p. 1611, June 2009.
Qiao J, Wang N and Ngadi M O, “Non-destructive inspection of Chinese
pear quality based on hyperspectral imaging technique,” J. Transactions
of the Chinese Society of Agricultural Engineering. p. 1, February 2007.
Jinjun Xia, Amanda Weaver, and David E. “Distribution of optical
scattering properties in four beef muscles,” J. Sensing and
Instrumentation for Food Quality and Safety. p. 75, February 2008.
Cluff K, Naganathan G K, and Subbiah J. “Optical scattering in beef
steak to predict tenderness using hyperspectral imaging in the VIS-NIR
region,” J. Sensing and Instrumentation for Food Quality and Safety. p.
189, February 2008.