Food Measure (2012) 6:3–11
DOI 10.1007/s11694-012-9126-z
ORIGINAL PAPER
Detection of fusarium damage in Canadian wheat using
visible/near-infrared hyperspectral imaging
Muhammad A. Shahin • Stephen J. Symons
Received: 30 May 2011 / Accepted: 27 March 2012 / Published online: 27 October 2012
Ó The Queen in Rights of Canada 2012
Abstract Fusarium damage in wheat may reduce the
quality and safety of food and feed products. In this study,
the use of hyperspectral imaging was investigated to detect
fusarium damaged kernels (FDK) in Canadian wheat
samples. More than 5,200 kernels, representing seven
major Canadian wheat classes, with varying degree of
infection symptoms ranging from sound through mild to
severe were imaged in the visible-NIR (400–1,000 nm)
wavelength range. Partial least squares discriminant analysis (PLS-DA) was used to segregate kernels into sound
and damaged categories based on kernel mean spectra. A
universal PLS-DA model based on four wavelengths was
able to detect FDK in all seven classes with an overall
accuracy of 90 % and false positives of 9 %.
Keywords
Wheat Fusarium Spectral imaging
Introduction
Appearance is the single most important factor that determines the value of grains. A number of grading factors
adversely affect the appearance of cereal grains. The adverse
effects of these grading factors on the end use quality of both
the common wheat and amber durum wheat have been welldocumented [1, 2]. Fusarium head blight (FHB), also known
M. A. Shahin (&) S. J. Symons
Grain Research Laboratory, Canadian Grain Commission,
1404-303 Main Street, Winnipeg, MB R3C 3G8, Canada
e-mail: muhammad.shahin@grainscanada.gc.ca
S. J. Symons
e-mail: stephen.symons@grainscanada.gc.ca
as scab or tombstone, is a fungal disease that may infect a
number of small grain cereals such as wheat, barley, and oats
[3]. The principal causal agent of FHB is Fusarium graminearum Schwabe [4]. FHB is favoured by wet weather at
flowering, but can infect the grain until harvest, given suitable
conditions for infection. Infection at the early stages of seed
development causes the greatest physical damage to the seed
and the highest levels of mycotoxin production [5, 6].
Fusarium damaged kernels (FDK) usually contain mycotoxins such as deoxynivalenol (DON), historically referred to as
vomitoxin, which may cause serious health problems.
Fusarium infection may have detrimental effect on flour
colour, ash content, and baking performance as well as other
quality and safety issues [7, 8]. A positive correlation between
fusarium damage and DON levels has been found [9].
Fusarium-damaged wheat is typically characterized by
thin or shrunken chalk-like kernels. In the Canadian grading
system, kernels with a white or pinkish fungal growth, no
matter how small, anywhere on the kernel are recognized as
Fusarium-damaged kernels (http://www.grainscanada.gc.
ca/oggg-gocg/04/oggg-gocg-4e-eng.htm#r), contrasting with
the USDA definition which considers only those kernels that
are chalk-like as scabby or tombstone kernels. For grading, a
representative sample is visually inspected for kernels
showing evidence of Fusarium spp. infection. This process is
slow when only slight damage is apparent as inspectors have
to use a 109 magnifying lens to examine each suspect seed
for the degree of mould growth. Severely damaged FDK
kernels can be easily detected by human inspectors, however,
visual detection of fusarium damage at an early stage is
challenging. Fast and accurate instrumental methods are
required to meet the needs of grain industry.
Several laboratory methods are available for detection
and measurement of moulds and mycotoxins in cereal
grains as well as flour samples including liquid and gas
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4
chromatography [10, 11], mass spectrometry [12], and
enzyme-linked immunosorbent assay [13]. However, these
methods are not suitable for rapid online inspection and
quality assurance protocols. Rapid inspection or sorting
methods for grain are typically based on kernel density using
a gravity table [14] or optical properties [15]. Combination
of gravity separation and optical sorting have been reported
for DON decontamination [16]. Metal–oxide–semiconductor sensors, commonly known as electronic nose, have
shown potential as a screening tool to distinguish between
DON contaminated and non-contaminated wheat samples
[17]. Near-infrared (NIR) spectroscopy has been used to
model DON levels in whole grain bulk samples with only
moderate success [18]. NIR reflectance has been investigated to measure DON concentration in single kernels [19].
Using NIR spectroscopy of individual kernels, sound and
FDK were segregated with high accuracy (95–97 %) under
controlled laboratory conditions [20]. In contrast, test results
using this technique on commercial samples under commercial sorting operational conditions had a much lower
accuracy (50 %) [21]. NIR absorbance characteristics of
various concentrations of DON as well as of sound and
fusarium damaged single wheat kernels have also been
investigated [22]. This study indicated that NIR spectrometry in the 1,000–2,100 nm range could estimate DON levels
in kernels having more than 60 ppm DON.
Hyperspectral imaging in the shortwave infrared (SWIR)
range (1,000–2,500 nm) has shown potential for detecting
fungal contamination in wheat and fusarium damage in
maize corn and wheat [23–26]. High cost of cameras sensitive in the SWIR range has been a limiting factor in the
development of commercially viable applications. Research
has shown that accuracy of fungal detection in wheat grain
with hyperspectral imaging in the 420–1,000 nm range
could be as good as in the 420–2,500 nm range [27].
Recently, the use of high-power bichromatic light emitting
diodes (LEDs) has been reported to achieve moderate levels
of overall accuracies (50–85 %) for detection of fusarium
damaged wheat kernels [28].
In a previous study, hyperspectral imaging in the visible-NIR range was used to detect FDK in Canada Western
Red Spring wheat [29]. Using a principal component
analysis (PCA) based approach, an FDK detection rate of
92 % was achieved using six wavelengths within
450–950 nm. This study expands their method to multiple
classes of Canadian wheat that vary significantly in physical characteristics of kernels. The objectives of this study
were (a) to develop a universal model to detect varying
degrees of fusarium damage in several major classes of
Canadian wheat using hyperspectral imaging in the visibleNIR wavelength range, (b) to validate the model performance on multiple classes of wheat as well as on independent samples from a different source collected over
123
M. A. Shahin, S. J. Symons
multiple crop-years, and (c) to identify a reduced set of
significant wavelengths for future development of a low
cost imaging system for detection of FDK in wheat.
Materials and methods
Samples
A set of 5,221 individual kernels of seven major classes of
Canadian wheat were hand picked from commercial samples
of 2009 crop year. The classes of wheat used in this research
included Canada Western Red Spring (CWRS), Canada
Western Amber Durum (CWAD), and Canada Western Red
Winter (CWRW) from the western region and Canada
Eastern Red Spring (CERS), Canada Eastern Soft Red
Winter (CESRW), Canada Eastern Hard Red Winter
(CEHRW), and Canada Eastern White Winter (CEWW)
from the eastern region. Samples for each class of wheat
covered a range of Fusarium damage from sound (no damage) through slightly-damaged to severely-damaged over a
wide range of quality from milling grade to feed. All the
kernels were individually inspected and scored by trained
grain inspectors (Industry Services Division, Canadian Grain
Commission) as sound (SND) or FDK. The FDK category
comprised of both severely-damaged (SVR) and mildlydamaged (MLD) kernels based on the extent of fusarium
damage. Mild damage was characterized as chalky-white
kernels with fungal or mycelial growth around the germ and
in the broadened crease, while severe damage was characterized as shrivelled chalky-white kernels with abundant
mycelial growth on both seed surfaces with some pink discoloration at the germ. Kernels with no visible symptoms of
damage were characterized as sound. This set of kernels was
randomly divided into two subsets of equal size namely a
calibration set and a validation set (Table 1a). The calibration set contained 2,611 kernels consisting of 1,073 SND and
1,538 FDK (795 MLD and 743 SVR combined). The validation set contained 2,610 kernels consisting of 1,074 SND
and 1,536 FDK (794 MLD and 742 SVR). Another independent set of 799 kernels of CWRS wheat, the prediction
set, was collected from the harvest survey samples received
at the Grain Research Laboratory (GRL, CGC) during 2008
and 2009. The prediction set consisted of 399 SND and 400
FDK (200 MLD, 200 SVR) kernels (Table 1b).
Hyperspectral imaging system
A push-broom type hyperspectral imaging system (VNIR
100E; Lextel Intelligence Systems, Jackson, MS, USA) in
the visible-NIR wavelength range (400–1,000 nm) was
used for spectral measurements of wheat kernels. The
imaging system consisted of a prism-grating-prism
Detection of fusarium damage in Canadian wheat
5
Table 1 (a) Class-by-class sample distribution in calibration and validation sets, (b) sample distribution in the prediction set
(a)
Wheat class
Calibration set
SNDa
Validation set
MLDb
SVRc
Total
SND
MLD
SVR
Total
CEHRW
180
133
135
448
180
135
132
447
CERS
100
75
75
250
100
75
75
250
CESRW
177
131
126
434
178
129
127
434
CEWW
143
108
103
354
143
107
104
354
CWAD
139
103
98
340
139
102
98
339
CWRS
174
127
87
388
174
127
88
389
160
1,073
118
795
119
743
397
2,611
160
1,074
119
794
118
742
397
2,610
CWRW
Total
(b)
Wheat class
CWRS
a
Prediction set
SNDa
MLDb
SVRc
Total
399
200
200
799
Sound kernel with no damage
b
FDK with mild symptoms
c
FDK with severe symptoms
spectrograph (ImSpector V10E; Specim, Oulu, Finland), a
high-resolution 14-bit CCD camera (PCO Imaging, Germany), a motorized C-mount focusing lens, and a personal
computer. The motorized lens assembly moved in front of
the camera allowing for imaging stationary samples. Two
250w quartz-tungsten-halogen lamps were used for sample
illumination. Power to each lamp was regulated through a
radiometric power supply (M-69931; Newport Oriel,
Stratford, CT, USA).
Image acquisition and calibration
For imaging, singulated wheat kernels, in batches of 24–36
per image, were placed crease-down on a neutral-grey
plastic board and hyperspectral images were collected in
the diffuse reflectance mode. Each hyperspectral image
(also known as hypercube) captured was 800 by 400 pixels
by 218 wavebands within 400–1,000 nm range at a spectral
resolution of approximately 2.75 nm. Each kernel was
approximately 1,800 pixels in area with a spatial resolution
of 0.028 mm per pixel in both x and y directions. The
exposure time was set at 60 ms.
Dark current and white light reference images were
collected before imaging each sample to calibrate spectra
at each pixel as percent reflectance value. A polytetrefluoroethylene panel with 99 % reflectance (Spectralon,
Labsphere, USA) was used to collect white light reference
images. Dark current response images were collected with
the lamp off and a cap covering the focusing lens.
Calibrated reflectance images (R) were calculated by using
Eq. (1).
R¼
Iraw Idark
Iwhite Idark
ð1Þ
where Iraw is the non-calibrated original image of a sample,
Iwhite is the image of the white reference, and Idark is the
dark current image. Calibrated hypercubes were subset to
keep 181 bands between 450 and 950 nm for further
analyses. Data below 450 nm or above 950 nm were
excluded due to the presence of excessive noise in these
wavebands.
Image processing and spectral data extraction
During a previous study, Shahin and Symons [29] were
able to successfully separate CWRS wheat kernel object
from the image background by thresholding the image
band at 600 nm whereby pixels with reflectance intensity
values greater than 10 % were labelled as kernels and
pixels with values less than 10 % were labelled as background. In order to determine if the same methodology
would work for multiple wheat classes, kernel spectra for
all seven classes were examined against the image background (Fig. 1). Based on these observations, a binary
mask image was created for each hypercube to exclude
image background from the calculations of kernel mean
spectra for further analyses. For each kernel in an image, a
representative spectrum was computed as the average of all
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M. A. Shahin, S. J. Symons
1.8
Reflectance, %
80
60
BKG
CEWW-FDK
CEWW-SND
CESRW-FDK
CESRW-SND
CWRW-FDK
CWRW-SND
CWRS-FDK
CWRS-SND
CEHRW-FDK
CEHRW-SND
CWAD-SND
CWAD-FDK
40
20
0
450
550
650
750
850
Normalized reflectance
100
1.4
1
CEHRW-SND
CESRW-SND
CWRS-SND
CWRW-SND
CEHRW-FDK
CESRW-FDK
CWRS-FDK
CWRW-FDK
0.6
0.2
450
550
650
750
850
950
Wavelength, nm
950
Wavelength, nm
Fig. 1 Spectral response of image background (BKG, thick black)
and kernels (SND and FDK) of different wheat classes (in color)
(Color figure online)
Fig. 2 Mean-normalized spectra for sound (SND) and FDK (dotted
lines) kernels of different wheat classes
pixel spectra within the kernel boundary. A macro was
written in ENVI ? IDL software (ITT Visual Information
Solutions, Denver, CO, USA) to automate the process of
calculating kernel mean spectra for each kernel in all the
images in a batch mode using in-built ENVI subroutine
envi_stats_doit whereby a kernel mean spectrum is computed as the average of all pixel-spectra within the
boundary of a kernel.
Partial least squares (PLS) regression model
The kernel mean spectra along with the inspector FDK
scores were ported into the Unscrambler (version 10.0.1,
CAMO Software AS, Norway) for further analyses. The
kernel spectra were mean-normalized by dividing each
spectrum with its mean value computed along the wavelength direction to minimize the effect of any lighting
inconsistencies. Spatial variability of incident light in the
image plane is a natural phenomenon. The intensity is
highest at the center of the illuminated area and falls off
gradually with distance from the center [30]. As a result,
kernels at different locations in the image receive different
illumination. Mean-normalization minimizes this effect by
bringing all pixel-spectra on a level plain field irrespective
of location in the image (Fig. 2). A PLS model was
developed with all the 181 bands in the calibration set as
input variables and inspector scores (1 for SND, 2 for
FDK) as the target values. Regression coefficients of this
all-band PLS model were examined to select a reduced set
of important wavelengths/wavebands (called ‘‘ibands’’)
without compromizing performance of the model. For this
purpose, a set of 13 ‘‘ibands’’ centered at peaks and valleys
of the regression coefficients’ plot was initially identified preserving the overall behaviour of the regression
123
Regression coefficient
3
639
coeffs
ibands
sbands
2
494
1
853
717
903 942
0
819
-1
917 950
578
450
678
-2
-3
450
883
550
650
750
850
950
Wavelength, nm
Fig. 3 Regression coefficients of the all-bands PLS model showing
the important wavelengths/bands (ibands) that can approximate the
overall behaviour of the plot, and prominent wavelengths (sbands)
coefficients’ function (Fig. 3). Using the ‘‘ibands’’ as the
input variables, a second PLS model was developed to
verify their ability to perform in comparison with the allband model. The ‘‘ibands’’ were further analysed for their
contribution in order to select minimum number of bands
required in a model without compromising the model
performance. The ‘‘ibands’’ (labelled as B1–B13) were
ranked based on their ability to discriminate between SND
and FDK categories using proc stepdisc of the SAS software (version 9.1.3; SAS Institute Inc., Cary, NC, USA).
The proc stepdisc procedure performs a stepwise discriminant analysis to select a subset of the quantitative
variables for use in discriminating among the classes. The
proc stepdisc procedure can use forward selection, backward elimination, or stepwise selection [31]. Variables are
chosen to enter or leave the model according to one of two
criteria: (1) the significance level of an F test from an
analysis of covariance, where the variables already chosen
act as covariates and the variable under consideration is the
dependent variable, and (2) the squared partial correlation
Detection of fusarium damage in Canadian wheat
7
for predicting the target variable, controlling for the effects
of the variables already selected for the model. Forward
selection with the default entry criteria (significance level
for entry, SLE = 0.15) was used in this research which
begins with no variables in the model. At each step, proc
stepdisc enters the variable that contributes most to the
discriminatory power of the model as measured by Wilk’s
Lambda or Average Squared Canonical Correlation
(ASCC). When none of the unselected variables meets the
entry criterion, the forward selection process stops.
After variable selection and ranking, additional PLS
models were developed with the calibration set using various combinations of selected ‘‘ibands’’ as input variables
based on their ranking (Table 2). The modeling process
continued, with each subsequent model using fewer most
significant bands, until the model performance reduced
noticeably. Performance of all these models was compared
with reference to the all-band model in order to determine
the best model (Table 3)—the one that requires the least
wavebands without compromising the performance in
terms of a low root-mean-squared-error (RMSE), a high
coefficient of determination (R2) as well as a high accuracy
of kernel classification.
Kernel classification
In the output of the PLS model, kernels were classified as
sound (SND) or damaged (FDK) based on the value being
less or greater than a threshold value, respectively. A
threshold value of 1.5 (half way between 1 for SND and 2
for FDK) was used to discriminate between SND and FDK
classes. Performance of the beast PLS based discriminant
analysis (PLS-DA) model was evaluated in comparison
with the inspector scores in terms of classification accuracy
for SND and FDK (MLD and SVR) kernels, overall as well
as on class-by-class basis. False positives (FP) were
determined as the percentage of SND kernels misclassified
as FDK kernels. The best model was tested on two independent datasets not used for model development—(1) the
validation set comprising of samples of all seven classes,
and (2) the prediction set consisting of CWRS samples
from a different source collected over two crop years.
Results and discussion
Spectral characteristics
Figure 1 shows the un-normalized reflectance spectra of
sound (SND) and FDK for various classes of wheat as well as
the image background (BKG). Each spectrum shown is the
average of 100 pixel spectra in the respective category. The
image background spectrum has a near-zero response in the
visible spectral range whereas the kernel spectra for both
SND and FDK for all classes are different. These observations
are inline with the previous findings reported earlier for
CWRS wheat [29]. These spectral differences allow the
image background to be separated from kernels of all seven
classes of wheat by thresholding an image band in the visible
range. The same threshold value, 10 % of the reflectance
Table 2 Summary of band/wavelength selection with the SAS procedure stepdisc
Step
Band in the model
Wavelength (nm)
Partiala R2
F value
Pr [ F
ASCC
Contributionb (%)
0
1
None
B2
–
494
–
0.4908
–
2514.50
–
\0.0001
0
0.4908
0
49.08
2
B3
578
0.0316
85.20
\0.0001
0.5069
1.61
3
B4
639
0.0292
78.52
\0.0001
0.5213
1.44
4
B5
678
0.1737
547.95
\0.0001
0.6045
8.32
5
B8
853
0.0201
53.51
\0.0001
0.6124
0.79
6
B1
450
0.0023
5.95
0.0147
0.6133
0.09
7
B7
819
0.0063
16.46
\0.0001
0.6157
0.24
8
B13
950
0.0018
4.64
0.0314
0.6164
0.07
9
B10
903
0.0012
3.23
0.0725
0.6169
0.05
10
B11
917
0.0026
6.77
0.0093
0.6179
0.1
11
B9
883
0.0039
10.17
0.0014
0.6194
0.15
12
B12
942
0.0050
12.94
0.0003
0.6213
0.19
13
B6
717
0.0009
2.43
0.1188
0.6216
0.03
The bands are listed in the order they were selected by the selection procedure
Pr probability, ASCC Average Squared Canonical Correlation
a
Partial R2 of a band at the time (step) of selection when bands with higher levels of significance, if any, are already in the model
b
Significance of bands contributing to model as determined by their relative contribution to ASCC
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M. A. Shahin, S. J. Symons
Table 3 PLS regression model performance for various number of wavebands and number of PLS factors in the model (calibration and
validation sets)
Spectrum (wavelength, nm)
All bands (450–950)
PLS factors
Calibration
Validation
RMSEC
R
2
%Acc
a
RMSEV
R2
%Acca
11
0.3028
0.6212
90.3
0.2992
0.6304
90.8
13 bands (450, 494, 578, 639, 678, 717, 819,
853, 883, 903, 917, 942, 950)
9
0.3031
0.6206
90.5
0.2989
0.6312
91.0
5 bands (494, 578, 639, 678, 853)
5
0.3065
0.6119
90.0
0.3011
0.6257
90.7
4 bands (494, 578, 639, 678)
4
0.3094
0.6045
89.8
0.3051
0.6156
90.5
3 bands (494,578, 639)
3
0.3404
0.5213
86.0
0.3393
0.5345
86.9
a
Accuracy of classification into sound and FDK categories averaged over all seven classes of wheat
value in the image band at 600 nm, worked for all seven
classes tested in segregating the kernels from the background.
While the un-normalized reflectance spectra proved
useful in segregating kernel objects from the image background, spectral differences between SND and FDK kernels
were not readily obvious due to between class and within
class variations as well as variations due to illumination
inconsistencies within the image plane. Mean-normalization
of spectra enhanced the differences between SND and FDK
kernels by minimizing the effect of kernel location in the
image plane (Fig. 2). In contrast, spectral differences due to
kernel condition (SND or FDK) could not be visualized from
un-normalized spectra (Fig. 1). It can be observed that the
shape of the normalized spectral curves for sound kernels is
different from that for the FDK kernels. None of the spectra,
however, had any distinct absorption bands within the entire
wavelength range (450–950 nm). Spectra of the sound kernels started off with lower intensities but rose quickly with a
steeper slope between 450 and 800 nm range than those of
the damaged kernels. The spectral response of FDK kernels,
in general, exhibited approximately monotonic gradual
increase within the spectral region of 500–800 nm. The
spectral response of mildly damaged kernels (MLD) (not
shown) would be expected to appear as an intermediate
transition spectra between those for the sound and severely
damaged ones. These differences were similar in all seven
classes of wheat investigated. A simplistic scenario is presented in Fig. 2 using one spectrum for each class of wheat.
It would be practically impossible to visualize these differences from a plot of a large number of spectra from kernels
of multiple classes with varying degree of damage. Detection of such differences from larger datasets required
advanced data modeling techniques such as multivariate
statistics or chemometrics.
PLS regression models
A PLS regression model using all 181 wavelengths within
450–950 nm range as the input variables and FDK scores
123
(SND or FDK) as the target output achieved an R2 of
0.6212 and an RMSEC of 0.3028, which translated to an
overall 90 % accuracy of kernel classification for the calibration set. Similar performance of this ‘‘All-bands’’
model was observed for the validation set (R2 = 0.63,
RMSEV = 0.30, accuracy = 91 %). The plot of the
regression coefficients for the All-bands model is shown in
Fig. 3, which indicates that the overall behaviour of the
regression coefficients’ function can be well approximated
by 13 wavelengths/bands representing peaks and valleys on
the plot. These 13 important bands are marked as ‘‘ibands’’
in Fig. 3. A PLS model based on 13 ‘‘ibands’’ achieved an
R2 of 0.6206 and an RMSEC of 0.3031 leading to 90 %
accuracy of kernel classification, a very similar performance to that observed for the all-bands model. This
indicated that these 13 bands contained nearly as much
information as the 181 bands in the full spectrum between
450 and 950 nm. Hence, a model based on these 13 bands
or a subset thereof could be used for kernel segregation
based on Fusarium damage. Four out of 13 ibands appeared
more prominent than others as can be seen in Fig. 3 (circled and marked as ‘‘sbands’’).
Wavelength selection is an important step towards
developing a relatively inexpensive and affordable multispectral system. The SAS procedure stepdisc selected all
13 ‘‘ibands’’ as statistically significant variables. Ranking
of ‘‘ibands’’ based on their respective partial R2 values and
significance of F value is presented in Table 2. The bands
are listed in the order in which they were selected by the
selection procedure. Discriminatory power of the model is
indicated by ASCC. Contribution of a band towards model
performance is determined by change in ASCC when that
band entered the model. B2 being the most significant band
with largest F value was the first to be selected in Step 1
while B6 being the least significant band was selected the
last in Step 13. In Step 2, with B2 already in the model, B3
was selected because its F statistics was the largest among
all bands not in the model. In the same way, B4 was
selected in Step 3. B5, despite its larger contribution
Detection of fusarium damage in Canadian wheat
9
(Table 2, column 8) towards model’s discriminatory power
(column 7) than B3 and B4, ranked lower than B3 and B4
because B5 had smaller F value than B3 in Step 2 and B4
in Step 3. Significance of B5 improved in Step 4 after B2,
B3 and B4 were already in the model. Five bands (B2–B5,
B8) out of 13 ibands accounted for approximately 98.5 %
of the discriminatory power of the model, i.e., the model
achieved an ASCC of 0.6124 with these five bands out of
the final ASCC of 0.6216 with 13 bands. The other eight
bands (listed below B8 in Table 2) collectively contributed
very little (\1.5 %) towards the discrinatory power of the
model. Four most significant bands (B2–B5) that contributed 97.25 % towards the discriminatory power of the
model were the same previously identified as ‘‘sbands’’
(Fig. 3). These observations indicated that including more
than five bands in the discriminant model would not
improve kernel classification significantly. This can be
readily observed from the relative contribution of each of
the 13 bands in terms of percent change in the value of
ASCC when a band is added to the model (Table 2, last
column). These observations indicate that four (or five)
most significant bands listed in Table 2 may be sufficient to
develop a PLS-DA model for the segregation of SND and
FDK kernels.
Performance of various PLS regression models based on
different number of input bands is presented in Table 3.
Performance of a 4-bands PLS model (using 4 most significant wavelength bands as input variable) was not only
similar to that of a 5-bands model (based on 5 most significant wavelength bands), it was also comparable to the
all-bands and 13-bands models in terms of R2, RMSE and
accuracy of kernel classification both for the calibration
and validation datasets. Performance of a 3-bands model
(using 3 most significant wavelengths as input) was significantly lower—approximately 12 % increase in RMSE,
about 16 % decrease in R2 and 4 % decrease in accuracy of
kernel classification were observed in comparison with the
reference all-bands model. The 4-bands model had
approximately a 2.2 % increase in RMSE, about a 2.7 %
decrease in R2 value and a 0.3–0.5 % decrease in accuracy
of kernel classification in comparison with the reference
all-bands model. Hence, a 4-bands model using 4 most
significant wavelengths (494, 578, 639, and 678 nm)
determined by the SAS procedure stepdisc as input variables was considered the best of all the models tested as it
required the least number of wavelengths without considerable reduction in the model performance.
Kernel classification
Kernel classification results for SND and FDK categories
based on a 4-bands PLS discriminant analysis (PLS-DA)
model are presented in Table 4a. Overall for all the seven
classes tested, total accuracy of classification for SND and
FDK categories combined was 90 % for both the calibration
and validation sets. Detection accuracy of SND kernels was
Table 4 Kernel classification results for (a) a universal PLS-DA model and (b) the prediction set using PLS-DA model based on four
wavelengths/bands (494, 578, 639 and 678 nm)
(a)
Wheat class
Classification accuracy (%)
Calibration set
Total
Validation set
SND
FDK
MLD
SVR
Total
SND
FDK
MLD
SVR
CEHRW
92
92
92
83
100
93
95
92
84
99
CERS
89
84
92
85
99
90
82
95
91
99
CESRW
88
87
89
79
100
87
85
89
78
100
CEWW
94
93
94
88
100
93
94
93
87
99
CWAD
87
92
83
71
95
86
88
85
72
98
CWRS
89
96
84
74
98
90
94
87
77
100
CWRW
Overall
90
90
89
91
91
89
84
80
97
99
94
90
94
91
94
90
87
82
100
99
(b)
Classification accuracy (%)
Total
SND
FDK
MLD
SVR
Before bias correction
88.5
77.2
99.7
99.5
100
After bias correction
95.9
95.2
96.5
93.5
99.5
123
10
M. A. Shahin, S. J. Symons
SND
FDK
Probability
1
0.5
0
0.5
1
1.5
2
2.5
3
Output value
Fig. 4 Distributions of the 4-band model output for sound (SND) and
Fusarium damaged (FDK) kernels of CWRS samples in the prediction
set
91 % overall. Accuracy of FDK detection for severely
damaged kernels (SVR) was very high (99 % overall),
however, accuracy of detection for MLD was relatively low
(80–82 % overall). The overall FDK detection rate and false
positives respectively were 89 and 9 % for the calibration set
and 90 and 9 % for the validation set. Of the seven wheat
classes, the best rate of FDK detection (92–95 %) was found
for CERS wheat. This was also associated with the highest
level of false positives (16–18 %). The lowest performance
was observed for CWAD with the FDK detection between
83 and 85 % and false positives between 8 and 12 %. The
model performed exceptionally well on the winter wheat
classes (CEHRW, CEWW, CWRW) both in terms of high
rate of FDK detection (91–94 %) and low false positives
(5–8 %). For CWRS wheat, the rate of detection ranged
between 84 and 87 with very low false positives (4–6 %)
both for the calibration and validation sets.
Classification results of the 4-bands model for the prediction set (CWRS samples collected from a different source
collected over 2 years) are shown in Table 4b. Overall,
88.5 % accuracy of kernel classification was achieved. The
model performance was nearly perfect in terms of the rate of
FDK detection (99.75 %). Only one mildly damaged kernel
out of 400 FDK kernels was missed. Detection of SND
kernels, however, was extremely poor (77 %) leading to
very high false positives (23 %). This was a clear indication
of the model being biased towards FDK detection needing
some bias corrections in the form of adjusting the threshold
value separating SND and FDK categories. As seen in
Fig. 4, distributions of the SND and FDK categories intersect at the model output around 1.7 indicating that the cut-off
line separating the two populations be moved to 1.7 instead
of at 1.5 as initially set for the calibration dataset. After bias
correction (raising the threshold value from 1.5 to 1.7), the
model performance was significantly better and well balanced. The overall accuracy of kernel classification
increased to nearly 96 %. False positives dropped considerably from 22.8 % before bias correction to 4.8 % after bias
123
correction while maintaining a high rate of overall FDK
detection (96.5 %). Detection of SND, MLD and SVR categories exceeded 95, 93 and 99 %, respectively after bias
correction.
The PLS regression model based on the four selected
wavebands (494, 578, 639 and 678 nm) was comparable to
that based on the full-spectrum (450–950 nm) and resulted
in highly accurate kernel classification based on Fusarium
damage. These results have demonstrated that FDK kernels
with slight to severe symptoms of infection in multiple
wheat classes can be detected with four selected wavebands. The fact that a few specific wavebands are required
for the detection of FDK in multiple classes of wheat
suggests that it may be possible to solve this problem using
a low cost imaging system built around a monochrome
digital camera and a set of optical filters in a motorized
filter wheel. For industrial uptake, the lower cost of such a
multi-spectral approach would be appealing. Such a system
would initially have the potential to identify samples
requiring further chemical analysis for toxin contamination. Subsequent studies in this laboratory will continue to
explore this possibility.
Conclusions
A visible-NIR hyperspectral imaging system with a
450–950 nm spectral range was used to detect fusarium
damage in seven major classes of Canadian wheat. Partial
least squares discriminant analysis (PLS-DA) was used on
the kernel mean spectra to segregate kernels into sound
(SND) and FDK categories based on absence or presence
of Fusarium damage, respectively. Based on the results of
this research, it can be concluded that hyperspectral
imaging over 450–950 nm can be used to detect varying
degrees of fusarium damage in major classes of Canadian
wheat. Using PLS-DA, sound and damaged kernels can be
classified highly accurately with an overall FDK detection
rate of 90 % and false positives of 9 %. A set of four
wavebands can be used to achieve accuracies similar to
those with the entire range of 450–950 nm.
Acknowledgments The authors thank the Inspection Division of the
Canadian Grain Commission (CGC) for providing inspected samples
for this research. They would also like to thank Loni Powell of the
Image Analysis & Spectroscopy Program (CGC) for scanning the
samples.
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