Evaluation of Postharvest Senescence of Broccoli via Hyperspectral Imaging

Abstract

Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and waste. In this conducted study, we hypothesized that certain proteins and compounds, such as glucosinolates, could be used as one potential indicator to monitor the freshness of broccoli following harvest. To support our study, glucosinolate contents in broccoli based on HPLC measurement and transcript expression of glucosinolate biosynthetic genes in response to postharvest stresses were evaluated. We found that the glucosinolate biosynthetic pathway coincided with the progression of senescence in postharvest broccoli during storage. Additionally, we applied machine learning-based hyperspectral image (HSI) analysis, unmixing, and subpixel target detection approaches to evaluate glucosinolate level to detect postharvest senescence in broccoli. This study provides an accessible approach to precisely estimate freshness in broccoli through machine learning-based hyperspectral image analysis. Such a tool would further allow significant advancement in postharvest logistics and bolster the availability of high-quality, nutritious fresh produce.

Full text

Research Article
Evaluation of Postharvest Senescence of Broccoli via
Hyperspectral Imaging
Xiaolei Guo,
1
Yogesh K. Ahlawat,
2
Tie Liu ,
2
and Alina Zare
1
1
University of Florida, Department of Electrical and Computer Engineering, Gainesville, Florida, USA
2
University of Florida, Horticultural Sciences Department, Gainesville, Florida, USA
Correspondence should be addressed to Tie Liu; tieliu@ufl.edu and Alina Zare; azare@ece.ufl.edu
Received 14 March 2022; Accepted 8 April 2022; Published 9 May 2022
Copyright ©2022 Xiaolei Guo et al. Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons
Attribution License (CC BY 4.0).
Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers
due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the
freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and
waste. In this conducted study, we hypothesized that certain proteins and compounds, such as glucosinolates, could be used as
one potential indicator to monitor the freshness of broccoli following harvest. To support our study, glucosinolate contents in
broccoli based on HPLC measurement and transcript expression of glucosinolate biosynthetic genes in response to postharvest
stresses were evaluated. We found that the glucosinolate biosynthetic pathway coincided with the progression of senescence in
postharvest broccoli during storage. Additionally, we applied machine learning-based hyperspectral image (HSI) analysis,
unmixing, and subpixel target detection approaches to evaluate glucosinolate level to detect postharvest senescence in broccoli.
This study provides an accessible approach to precisely estimate freshness in broccoli through machine learning-based
hyperspectral image analysis. Such a tool would further allow significant advancement in postharvest logistics and bolster the
availability of high-quality, nutritious fresh produce.
1. Introduction
Broccoli (Brassica oleracea L. var. italica) is a nutritious veg-
etable that is also enriched in chemical compounds like glu-
cosinolates that can lower cancer risks [1]. Glucosinolates
function not only in plant protection, but also can reduce
the risks for certain cancers in those who consume plants
high in glucosinolates. Broccoli is usually harvested while
the inflorescence is still developing and is removed from
water and nutritional sources, which causes stress-induced
senescence. This senescence leads to faster chlorosis and
increases in protease activities that dismantle chloroplasts
and cause chlorophyll breakdown [2].
Senescence is a developmental process accompanied by
physiological and biochemical changes in transcripts, pro-
teins, and metabolites at discrete stages. One particularly
interesting class of metabolites are glucosinolates. Glucosin-
olates are present as glucosides in the brassicas and are
decomposed inside the plant cells during cutting, damage,
or chopping by the enzyme called myrosinase into smaller
sulphur containing compounds such as isothiocyanates [3].
The glucosinolate content in brassicas depends on
numerous factors, such as cultivar, harvest time, and storage
conditions. Furthermore, both pre- and postharvest factors
affect glucosinolate content in broccoli [2, 4]. It was reported
recently that harvest time is crucial for the evaluation of
glucosinolate contents in broccoli and harvesting at noon
maintains higher level of glucosinolates [4]. In harvested
broccoli, the actual quality, storability, and overall freshness
are quite uncertain unless changes such as chlorophyll and
carotenoid degradation are visible to human eyes [5].
Chlorophyll fluorescence and RGB (red, green, and blue)
color imaging analyses were used to monitor pigment
changes in broccoli during postharvest storage [6]. However,
objectively measuring the progression during the loss of
freshness in produce after harvest has always been an intrac-
table problem in postharvest handling of fruit and vegeta-
bles. A strategy to measure the glucosinolate accumulation
AAAS
Plant Phenomics
Volume 2022, Article ID 9761095, 12 pages
https://doi.org/10.34133/2022/9761095

would detect any early physiological and biochemical
changes of senescence before any visible signs occur that
would allow for the early determination of freshness thus
development of diagnostic tools for improvement of post-
harvest shelf life [7]. Therefore, sensors for initiation and
progression of deterioration are essential for monitoring
the physiological changes during postharvest storage in
broccoli as well as other fruit and vegetables.
The rapid advancement of optical sensors such as multi-
spectral imaging technologies has significantly impacted
agriculture [8]. HSI provides both spatial and spectral infor-
mation about an object and consists of thousands of pixels in
a two-dimensional array, with each pixel containing a spec-
trum corresponding to a specific region on the surface of the
sample. These spectra vary according to the material and
chemical compositions. Introduction of these spectra pro-
vides the development of mathematical models to estimate
chemical compositions or functional class of a sample asso-
ciated with each pixel. HSI has been used in plethora of
applications in the study of brassicas, to detect and quantify
components and quality parameters in a wide range of bio-
logical matrices. For instance, recent studies estimated
canola seed maturity using a UAV-mounted hyperspectral
camera [9], quantified the oil content and fatty acid in Bras-
sica seeds [10], and detected the nitrogen concentration in
oilseed rape leaf with a laboratory hyperspectral imaging
system [11].
Numerous techniques have been proposed to correlate
hyperspectral data with fruit and vegetable quality and safety
evaluations, including feature extraction via principal com-
ponent analysis (PCA) [12, 13], linear discriminant analysis
(LDA) [13, 14], partial least squares regression (PLSR)
[14–17], classification via support vector machines (SVM)
[15], random forests (RF) [16], and artificial neural networks
(ANN) [15, 18] and recent work on spectral unmixing tech-
niques [19].
To meet the need for detection of senescence progres-
sion through imaging sensors such as HSI, we leveraged
HSI, applied spectral unmixing, and target detection algo-
rithms to predict senescence at an early stage of symp-
toms. Compared with the previous works in
dimensionality reduction and multivariate classification,
our main contributions are as follows: (i) the application
of spectral unmixing for physically meaningful and inter-
pretable feature extraction; (ii) we estimated a spectral sig-
nature across all wavelengths for broccoli tissue with
different glucosinolates concentration; and (iii) we gener-
ated improved maps which visualize the glucosinolates
changes in broccoli. Here, we propose the accumulation
of glucosinolates that correlated with onset and progres-
sion of senescence in broccoli following harvest. We first
conducted biochemical measurements and gene expression
analysis to illustrate that glucosinolates can serve as a
freshness indicator before noticeable color changes in
broccoli. Then, we demonstrated the feasibility of the
HSI to determine the glucosinolate concentration of broc-
coli during postharvest storage. We concluded that a com-
bination of glucosinolate analysis and HSI can evaluate the
freshness signatures of postharvest broccoli.
2. Materials and Methods
2.1. Tissue Collection and Preparation. The crowns of freshly
grown broccoli (cultivar, Emerald Crown) were manually
harvested from local farms in Hastings, Florida. All broccoli
florets were selected with similar size and small beads in this
study. The florets were subjected to different storage condi-
tions: 4
°
C for cold treatment and 25
°
C for room tempera-
ture. Four biological replicates of broccoli florets were
sampled for tissue collection. Tissue samples were collected
from the broccoli florets every other day over a twelve-day
period. The samples were wrapped in aluminum foil, imme-
diately frozen in liquid nitrogen, and then either stored at
-80
°
C for later quantitative PCR or freeze-dried for HPLC.
The similar broccoli samples were chosen for the spectral
imaging analysis.
2.2. Extraction of Total Glucosinolates for HPLC
Quantification. HPLC analysis was performed to quantify
the glucosinolates. Total glucosinolates were measured
according to previously reported methods, with some modi-
fications ([20]. Briefly, raw materials from harvested broccoli
florets that were stored at -80
°
C were taken out to be dis-
persed in liquid nitrogen. Broccoli tissue samples were
weighed, and 100 mg fresh tissue was crushed to a fine pow-
der using a mortar and a pestle. The tissue powder was dis-
solved in 1 ml of 50% methanol in a 1.5-ml microcentrifuge
tube. The tubes were kept at 65
°
C for 1 hour in a water bath.
Samples were then centrifuged at 15000×gfor 10 min. The
supernatant was filtered through a 0.22 μm pore size hydro-
philic PTFE syringe filter (Sigma Aldrich, USA). The gluco-
sinolate contents were estimated based on the peak area at
220 nm [21]. Four biological replicates were used for each
time point.
2.3. RNA Extraction and Quantitative Real-Time PCR. Floret
tissue samples from days 1, 3, and 5 were chosen for expres-
sion analysis of genes related to the glucosinolate biosyn-
thetic pathway. Total RNA was isolated from broccoli
floret using the TRIZOL (Ambion, Life Technologies,
USA) method with DNase treatment (Turbo DNA free,
Thermofisher, USA). First-strand cDNA synthesis from
1μg of total RNA was performed using a reverse transcrip-
tion kit (Applied Biosystems, Foster City, CA). For the quan-
titative real-time RT-PCR, the primers were designed using
Primer-Quest from Integrated DNA Technologies (IDT).
The primers for the genes of glucosinolate biosynthetic path-
way are listed in Table 1. Real-time PCR reaction was per-
formed in an Applied Biosystems qPCR machine
(Thermofisher, USA). The total reaction volume was 10 μl
for each gene, and the reactions were run in triplicate with
thermocycler conditions as follows: 95
°
C for 10 min, 45
cycles of 95
°
C for 30 sec, and 60
°
C for 30 sec. The relative
gene expression was calculated by ΔΔCT method according
to the qPCR machine manufacturer (Thermofisher, USA).
The Actin2 from broccoli was used as an internal control.
The experiment was repeated twice for expression studies.
2.4. Hyperspectral Image Acquisition. Broccoli hyperspectral
images were collected with a HinaLea Model 4200
2 Plant Phenomics

hyperspectral camera (TruTag Technologies, Emeryville,
CA). The acquired raw images were calibrated to relative
values with the white and dark reference:
Calibrated = Raw −Dark
White −Dark ,ð1Þ
and then converted to reflectance spectra with TruScope
Software (TruTag Technologies, Emeryville, CA). The cam-
era covered the visible wavelengths ranging from 400 nm
to 700 nm (corresponding to chlorophyll absorption and
sensitive development), the red-edge (chlorophyll content
and stress conditions), and the near-infrared region
(700 nm-1000 nm, related to cell structure) with a resolution
at 4 nm (based on full-width at half maximum), resulting in
300 wavebands. 70 W halogen lamps (Malvern Panalytic al
Ltd, Malvern, UK) were used as illumination in the imaging
chamber. Within the chamber, each broccoli sample was
placed on a black plate with matte black sides that absorbs
redundant light to minimize scattering. Hyperspectral mea-
surements of four biological replicates were carried out at
days 1, 3, 5, 8, 10, and 12 over the twelve-day period. The
florets were imaged from the side where the lower part of
the broccoli crown is near to the pedicle. The similar area
of tissues were collected for glucosinolates analysis.
2.5. Hyperspectral Image Analysis. Considering that the HSI
can detect changes in chemical compositions under various
abiotic stress conditions, we reasoned that the initiation
and progression of glucosinolates accumulation during post-
harvest senescence can be monitored through HSI analysis.
To achieve this goal, we performed time course imaging
experiments on broccoli florets under room temperature
and refrigerated storage conditions.
An overview of the analysis is shown in Figure 1, includ-
ing training and testing procedures. Both training and test-
ing phases started with several preprocessing steps. The
goal of training was to estimate parameters for feature
extraction and fit model for regression. Here, we compared
two feature extraction approaches based on (i) spectral
unmixing using the Sparsity Promoting Iterated Constrained
Endmember (SPICE) [22] and (ii) subpixel target detection
using the Multiple Instance Adaptive Cosine Estimator
(MI-ACE) [23, 24]. Using each of these feature extraction
approaches, the extracted features from each broccoli image
was paired with a corresponding glucosinolates measure-
ment and used to fit a multivariable linear regression
(MLR) model. The trained parameters and trained model
were then fed into a testing phase to predict the glucosino-
late concentration of testing samples that were not used dur-
ing training. The combination of feature extraction and
regression procedure was compared to the classical PLSR
approach. An overview of this approach is shown in
Figure 1.
2.6. Hyperspectral Image Preprocessing. For this study, pre-
processing of the measured spectra for noise and illumina-
tion variation was performed prior to analysis. Since the
illuminator was a single light source and did not evenly
cover the entire imaging surface, the center of the imaging
plane was brighter than the outer edge from Figures 2(a)–
2(c). The measured reflectance spectra consistently con-
tained higher levels of noise at the two ends of the wave-
length range as shown in Figure 2d. Thus, three steps were
taken to mitigate these issues. First, a median filter length
of 5 bands along the wavelength axis was applied to the
reflectance spectra. Second, responses below 500 nm and
above 900 nm were removed due to the high noise levels.
Finally, we applied l
2
normalization to reduce the magnitude
variation caused by the point light source and focus more on
the spectra shape [19, 25]. Specifically, we divided each spec-
tral signature by its l
2
norm [26]. The l
2
normalization treats
each spectrum as a vector and normalizes it to unit sphere in
the vector space. The normalized result can be illustrated by
comparing Figures 2(a), 2(d), and 2(e). In Figure 2(d), the
blue curve, which is sampled from the brighter center in
Figure 2(a), is greater than the red and orange curve. After
normalization, the magnitude variation was reduced in
Figure 2(e), and we can see that their spectra shape is consis-
tent since they are sampled from the identical broccoli sam-
ple. Although the reflectance is extremely low in Figure 2(e),
Table 1: List of primers for quantitative PCR performed for
glucosinolate biosynthesis pathway in broccoli.
Glucosinolate biosynthetic
genes Sequence
BO_ACTIN2-FORWARD TGGTCGTGACCTTACTGAC
TAT
BO_ACTIN2-REVERSE TCACTTGTCCGTCGGGTAAT
BO_ST5B2-FORWARD CCCATATACCCAACGGGTCG
BO_ST5B2-REVERSE CCCATGAACTCAGCCAACCT
BO_MAM1-FORWARD GGAATTATCCCTACCACCAGT
TC
BO_MAM1-REVERSE CAGAGGAGCAACATGAGAT
GAG
BO_CYP79F1-FORWARD GTTAGGACAAGCGGAGAAA
GA
BO_CYP79F1-REVERSE CCATCAATGTTCCAACCTCTA
AAC
BO_AOP2-FORWARD GTGAGGAGTGATGTCCGTA
AAG
BO_AOP2-REVERSE GCCTCAACTGGTAACTCGA
AA
BO_ESM1-FORWARD CCGGAAGTAGCGTTGTTTACT
BO_ESM1-REVERSE GTTAGGGTCGTCAAGGGATTT
BO_MAM3-FORWARD ATCGTCCGTACAACAAGTC
ATC
BO_MAM3-REVERSE GTATGTACTCTGGCCACCT
TTC
BO_ESP-FORWARD AGGACGATCGAGGCCTATAA
BO_ESP-REVERSE GAATCCAGCTCCACCTCTTT
BO_FMOGSOX1-
FORWARD
GGATTAATAGCGGCCAGAG
AG
BO_FMOGSOX1-REVERSE GCGGGTCGGATTCAGATTTA
3Plant Phenomics

the signal and noise ratio (SNR) remains the same since the
noise were reduced on the same scale.
After preprocessing the spectra, the regions of broccoli
florets were segmented from the remainder of the hyper-
spectral cube. This segmentation was accomplished in two
steps: (1) segmenting the broccoli sample from the black
background and (2) segmenting the floret from the stalk.
Spectral readings were analyzed from different regions of
the image for step 1 (Figure 2(a)). There were clear spectral
differences between broccoli and the black background
(Figure 2(d) for raw spectra and Figure 2(e) for preprocessed
spectra). Namely, the broccoli spectra (blue, red, and yellow
spectra corresponding to colored points in Figure 2(a)) have
a bump around 550 nm (visible bands of green) and a sharp
increase around 700 nm (near infrared/red edge), whereas
the spectra for the black background (purple, cyan, and green
1.5
Raw spectra
(a)
(d) (e)
(b) (c)
400 600
Wavelength (nm)
800 1000
1
Reflectance (%)
0.5
0
0.2
Pre-processed spectra
500 600 700
Wavelength (nm)
800 900
0.15
Reflectance (%)
0.1
0
0.05
Figure 2: RGB imaging segmentation procedure and processed spectra. (a) Broccoli sample placed on a black plate. (b) Segmented broccoli
head. (c) Segmented broccoli crown without stem. (d) Reflectance measured by HinaLea 4200 hyperspectral camera. (e) Preprocessed
spectra. The colors of spectra in (d-e) correspond to the colored points in (a).
Image pre-processing
Regression (MLR)
(a) (b)
Trained parameters
(for feature extraction)
Training images Glucosinolates
measurement
Trained model
(for regression)
Feature extraction:
(i) SPICE; (ii) MI-ACE
Image pre-processing
Feature extraction:
(i) SPICE; (ii) MI-ACE
Testing images Trained parameters
Regression (MLR)
Trained model
Testing prediction
Note:
SPICE and MI-ACE
are two feature
extraction
approaches
compared parallel
Figure 1: Flowchart of HSI data analysis. (a) Training procedure. (b) Testing procedure.
4 Plant Phenomics

spectra corresponding to colored points in Figure 2(a)) are
nearly flat up to 800 nm and then increase rapidly. We
should note that after preprocessing steps, the differences
in shape between broccoli and background are more distin-
guished in Figure 2(e). Given these significant spectral differ-
ences, the k-means clustering algorithms aim to iteratively
partition the pixel spectra into two groups (i.e., broccoli
and background in our case). Spectra were assigned to the
group with the closest cluster centroid in Euclidean distance.
The clustering result generated a binary mask image where
the mask for a pixel would be 1 if its corresponding spectra
belongs to the broccoli group; otherwise, the pixel mask
would be 0. Next, a morphological image closing operation
was applied on the mask image to connect any disconnected
points. The segmented results are shown in Figure 2(b).
Since the glucosinolate concentration was measured on
broccoli florets, we hypothesized that focusing on the spectra
of the broccoli head was generated stronger correlation than
analyzing the spectra of the entire broccoli (including the
stem). In order to segment the head from the stalk, we
applied the GraphCut algorithm [27] of the image segmenta-
tion toolbox in Matlab 2019b [28]. The algorithm was
seeded by providing a marking that denoted the broccoli
flower and the background including broccoli stem. The seg-
mentation took around 5 to 10 seconds for each image. The
segmentation results are shown in Figure 2(c).
The collected and preprocessed hyperspectral images
consisted of 200 spectral bands of size 968 × 608 pixels. Each
image usually covered around 40% to 80% of the broccoli
sample and contained approximately 0.3 million of pixels
per sample. Since the large data size will slow down the com-
puting, the spectra were down-sampled to 5,000 per seg-
mented broccoli sample via k-means clustering.
Specifically, spectra of all the broccoli pixels were clustered
into 5,000 groups, and each group spectra were represented
by their average. Down-sampling was applied to the two seg-
mentation scenarios being considered: (1) entire broccoli
and (2) broccoli florets, respectively.
2.7. Spectral Unmixing with the SPICE Algorithm. The
hyperspectral image is a high-dimensional image cube that
described each pixel as the radiance or the reflectance at a
range of wavelengths across the electromagnetic spectrum.
The spectrum of a pixel is usually determined by the mate-
rial of the object surface. These measured spectra are a mix-
ture of a set of constituent spectrum, also known as
endmembers. Spectral unmixing is the task defined as
decomposing a mixed spectra into a collection of endmem-
bers and their corresponding proportions, also known as
abundances [29]. A well-known spectral model (and the
most commonly applied to perform hyperspectral unmix-
ing) is the linear mixing model (LMM), which represents
each measured spectra as a convex combination of endmem-
bers [29]:
si=〠
M
k=1
aikek+εisuch that 〠
M
k=1
aik =1,a
ik ∈0, 1
½
,ð2Þ
where siis the spectra of pixel i, εiis the noise vector, Mis
the number of endmembers, ekis the kth endmember, and
aik is the corresponding abundance value. The objective of
unmixing is to estimate a set of endmembers and abun-
dances that can indicate the freshness level of the broccoli
being imaged. Particularly, the estimated endmembers are
supposed to represent the range of “freshness”levels in the
samples. Then, the associated abundances for the endmem-
bers corresponding to “fresh”can be viewed as a freshness
indicator. The endmembers and abundances were estimated
using the Sparsity Promoting Iterated Constrained End-
member (SPICE) algorithm.
The SPICE algorithm benefited from simultaneous esti-
mating the shape and number of endmembers as well as
their abundances [22]. The Matlab and Python implementa-
tion for SPICE can be found here: github.com/GatorSense.
In our experiments, since the estimated endmembers
highly depend on the parameter Γ(which serves as a param-
eter to determine the number of endmembers needed), a
range of Γvalues, starting from 10 to 150 in steps of 10, were
explored. We conducted 10 repetitions of 3-fold cross-
validation for 15 Γvalues. With the estimated endmembers,
the abundance feature can be derived from the training folds
to fit the MLR model. Note that the large variation of errors is
caused by the outliers in the training and validation folds.
There was a greater prediction error on validation folds with
a smaller Γ(Figures S3(a)-S3(b) and S3(f)-S3(g)), which
indicates overfitting. In other words, since Γdetermines the
number of estimated endmembers to be eliminated, a
smaller Γvalue results in a greater number of endmembers
and more parameters that need to be estimated (and
provide opportunities for overfitting). There was a tendency
of overfitting with an increasing number of endmembers,
M(Figure S3(c)-S3(d) and S3(h)-S3(i)). In addition, both
segmentation methods determined that Mshould be set at
3(M=3), since this value produced the most replications,
over 450 (Figures S3(e) and S3(j)).
2.8. MI-ACE to Detect Freshness Indicator. In addition to
spectral unmixing, we explored a target detection method
as an alternative feature extraction approach. In particular,
the Multiple Instance Adaptive Cosine Estimator (MI-
ACE) [15, 16] was investigated to detect a spectral signature
of “freshness.”In a hyperspectral image, the spectra siof an
individual broccoli pixel i can be considered an instance,
while all pixels of the entire broccoli sample (a group of
instances) can be considered as a bag. A bag of one broccoli
sample was labeled as positive if it contains high glucosino-
lates concentration, which meant that there existed at least
one instance corresponding with the freshness indicator;
otherwise, the bag was labeled as a negative. We set a thresh-
old of glucosinolates level as 50 to distinguish less fresh
(≥50) and fresh sample (<50). The threshold was decided
by the significant increasing of glucosinolate level from day
1 to day 3.
MI-ACE estimates a discriminative target signature tfor
the freshness indicator. Then, instance siwithin one bag
was assigned a confidence value ai, indicating the confi-
dence of highly correlated with the discriminative target
5Plant Phenomics

signature. Compared with unmixing approach, the MI-ACE
in discriminative target signature was easier to interpret,
implying that the freshness indicator can be distinguished
from the negative instances of broccoli with low glucosino-
lates level. In this experiment, the target signatures are esti-
mated in a similar setting of 3-fold cross-validation as
SPICE.
2.9. Correlating Abundance with Glucosinolate Concentration
Level. The estimated vectors a=fa1,a2,⋯,akg∈ℝ1×M for
each broccoli sample (where the value akis the average abun-
dance for unmixing, or average confidence for MI-ACE of all
pixels over the region of interest as ak= 1/N∑N
i=1aik , where N
denotes the number of pixels) were used to predict measured
glucosinolate concentration values. Specifically, a multivari-
able linear regression (MLR) model (Equation (3)) was fit
using least squares estimation approaches to predict the glu-
cosinolate concentration value:
glucosinolate ~ b + 〠
M
k=1
wk
ak,ð3Þ
where bis the bias, wkis the coefficient for ak,Mis the num-
ber of estimated endmembers for unmixing, or M=1for MI-
ACE.
2.10. PLSR Analysis. As a comparison approach, a PLSR
model was investigated and compared with the above SPICE
and MI-ACE approach. The average spectra of training sam-
ples were considered predictors matrix Xwith dimension
(Ntraining ×Bbands), where Nt raining denotes the number of
training samples and Bbands denotes the number of wave-
length (Bbands = 200 in our case). The glucosinolate measure-
ments were considered a response vector Ywith dimension
(Ntraining ×1). The partial least square is a latent variable
approach to find the relations between predictors and
response. The prediction of Ycan be written as follows [30]:

Y~Tβ,ð4Þ
where Tis the wavelength scores matrix and βis the regres-
sion coefficient. The regression was conducted using Matlab
2019b [28].
3. Results
3.1. Glucosinolate Content Increased during Postharvest
Senescence. When stored at room temperature, broccoli
started to display visible yellowing after five days of harvest.
There is no obvious color change found within a five-day
storage. To assess the possibility that glucosinolate levels
can be detected at the early stage of senescence and used as
a indicator for senescence, we performed HPLC analysis to
measure the total glucosinolate concentration during a
twelve-day period in broccoli at either room (25
°
C) or cold
(4
°
C) temperature. We found that there was a near-linear
increase in glucosinolate concentration over the 12-day
period when the broccoli was stored at room temperature
(Figure 3). However, the accumulation of glucosinolates
was not observed during cold storage. This data suggested
that there was a strong correlation between glucosinolate
levels and the progression of postharvest deterioration in
broccoli when stored at room temperature.
3.2. Glucosinolate Biosynthesis Transcript Levels Increased
during Postharvest Storage. To investigate whether the glu-
cosinolate accumulation was due to glucosinolate biosynthe-
sis and how the glucosinolate biosynthetic pathway was
affected during postharvest senescence, we carried out quan-
titative gene expression analysis on the key genes in the glu-
cosinolate biosynthetic pathway. The first reaction of
glucosinolate biosynthesis was catalyzed by two enzymes
methylthioalkylmalate synthases (MAM1 and MAM3).
MAM1 and MAM3 initiated the formation of glucosinolate
chain products. The transcripts of both enzymes that pro-
duce intermediates, like MAM1 and MAM3, were increased
(4.3-fold from day 1 to day 3 and 11-fold) from day 3 to
day 5 at room temperature. In cold conditions, MAM1 levels
were undetectable on day 3 but increased on day 5 by 5.3-fold
(Figure 4). This implied that MAM1 levels were increased in
higher proportions during room temperature storage. Similar
patterns were observed for the other genes encoded enzymes
such as epithiospecifier modifier 1 (ESM1), α-ketoglutarate-
dependent dioxygenase (AOP2), epithiospecifier protein
(ESP25), CYP79, and ST5, as their transcripts increased sig-
nificantly from day 0 to day 5. ESM1 and CYP79 modified
and catalyzed the conversion of amino acid to aldoxime.
However, under cold storage, the increase of transcript levels
in flavinmonosygenases (FMOGSOX2) from day 0 to day 5
was not significant. The FMOGSOX2, an enzyme, catalyzed
the conversion of methylthioalkyl glucosinolates to methyl-
sulfinylalkyl glucosinolates. This observation provided evi-
dence that changes in genes expression for the glucosinolate
biosynthetic pathway were associated with the freshness of
broccoli. Therefore, our results suggested that the production
of glucosinolate was triggered by transcriptional regulation of
glucosinolate biosynthesis during the postharvest storage.
This data further validated that there is a correlation between
postharvest senescence and glucosinolate production in
broccoli when stored at room temperature, suggesting that
the glucosinolate can be used to detect early senescence even
without any visible color changes during storage.
3.3. Visualization of HSI Analysis. To validate if HSI imaging
can detect the glucosinolate contents in broccoli, we per-
formed imaging analysis on broccoli at two storage condi-
tions after harvest and designed two approaches to test out
hypothesis. In Figure 5, it showed the HSI analysis on broc-
coli florets with two approaches, the SPICE (Figures 5(a) and
5(c)–5(d)) and the MI-ACE (Figures 5(b) and 5(e)–5(f))
algorithm.
First, the estimated endmembers via SPICE are shown in
Figure 5(a). “EM”is an abbreviation of endmember. The
error bar in Figure 5(a) illustrated the variance of estimated
endmembers over 10 replications of 3-fold cross-validation
as stated in Materials and Methods. It was noted that the
SPICE algorithm was unsupervised. The information about
6 Plant Phenomics

the estimated endmembers was inferred by examining the
abundance map. Figure 5(c) compared the abundance map
for the testing sample. The rows were grouped by time
course (1, 4, and 7 for day 1; 2, 5, and 8 for day 5; 3, 6,
and 9 for day 12), while each column was corresponding
to an estimated endmember (1-3 for EM1, 4-6 for EM2, 7-
9 for EM3). The numeric values in abundance map were
mapped into the color scale, where the blue denotes weak
abundance near the 0, and bright yellow denotes strong
abundance near the 1. Figure 5(d) shows the histograms of
abundance maps in the same layout (rows for time course
and columns for estimated endmembers). For instance,
Figure 5(d) (13) was the histogram of abundance values
showing in Figure 5(c)(1), indicating how much the broccoli
sample is correlated to EM1 in day 1.
Abundance maps and histograms shown in Figures 5(c)
and 5(d) are informative to classify estimated endmember
via SPICE. For example, the great abundance value (bright
yellow) in Figure 5(c) (2) and dense concentration of histo-
gram to 1 in Figure 5(d) (16) implied that the broccoli sam-
ple from day 1 was highly correlated with EM2 (shown in
Figure 5(a)). Thus, EM2 is potentially the “fresh endmem-
ber.”Similarly, Figure 5(c) (3) and Figure 5(d) (15) implied
that the broccoli sample from day 12 had a strong response
to EM1 (shown in Figure 5(a)). Thus, EM1 is potentially the
“least fresh endmember.”Taken together, the distribution of
abundance values were associated with each endmember
that provided information of freshness over the time course.
The visualization of the MI-ACE result is shown in
Figures 5(b), 5(e), and 5(f), in which (b) plotted the discrim-
inative target (less fresh) and background (fresh) signature
on broccoli florets. The background signature was estimated
by averaging the negative bag, which means the spectra of
fresh broccoli with low glucosinolate level. The discrimina-
tive signature was estimated via MI-ACE algorithm and
implied the difference between fresh and less fresh broccoli.
Similar to Figure 5(a), the error bar in Figure 5(b) illustrated
the variance of estimated signatures over 10 replications of
3-fold cross-validation. According to the Figure 5(b), the
increasing of glucosinolates resulted in several changes of
broccoli spectra, including a bump (increasing) around
580-700 nm (visible bands of yellow, orange, and red) and
a concave (decreasing) around 750-800 nm (near infrared).
The shape of discriminative target signature was consistent
with the estimated endmembers in Figure 5(a), where the
reflectance of the EM1 (least fresh) was greater around
580-700 nm and less around 700-800 nm compared with
EM2 (most fresh). Based on the discussion, 580-700 nm
and 750-800 nm are potential for accounting freshness of
broccoli. It is also worth noting that, by examining the vari-
ance in Figure 5(b), the discriminative signature has a
greater variance between 550 and 700 nm, which can be
explained by the variance on visual appearance, while the
variance between 700 and 900 nm is relatively smaller, which
means the detected signature are more consistent in the
near-infrared wavelengths. Our future work will include
investigating the most significant wavelength via band selec-
tion techniques.
Figures 5(e) and 5(f) show the confidence map and their
histograms for testing samples on day 1, day 5, and day 12.
The confidence values implied the score that each pixel
belongs to the target (less fresh). It shows in Figure 5(e) that
the confidence was increasing from day 1 (11) to day 12 (12)
and the concentration of corresponding histograms were
shifted from 0 in day 1 (22) to 1 in day 12 (24). This change
implied the tendency of getting less fresh over time course.
Therefore, the distribution of confidence values was corre-
lated with freshness over the time course, which was also
consistent with Figures 5(c)–5(d).
3.4. Correlating the Changes in Glucosinolate Levels in
Postharvest Broccoli through Hyperspectral Imaging. To cor-
relate the hyperspectral image with the glucosinolate levels
in postharvest broccoli, we investigated both the abundance
feature generated via SPICE, and the confidence value gen-
erated via MI-ACE. The glucosinolate concentration and
derived abundance feature as well as confidence value were
applied to fit the (MLR) model. In addition, we compared
0
50
100
150
200
250
DAY 1 3 5 7 9 11
25C
4C
Relative abundance for glucosinolates
Figure 3: Quantification of the glucosinolates in broccoli by HPLC. The total glucosinolate level in broccoli florets sampled on days 1, 3, 5, 7,
9, and 11 of storage at room temperature (blue) or in the cold (red). The y-axis is the abundance of total glucosinolate content. Data
represented means ± SE bars (n=4for each day).
7Plant Phenomics

the feature extraction + MLR model approaches with the
classical PLSR model. 6 fitted models with variables based
on different approaches were applied to the testing sample
to generate the residuals shown in Figure 6. The residuals
are calculated by Res =Y−
Y, where Ydenotes the
measured glucosinolates concentration and

Ydenotes the
predicted value. It can be seen from Figure 6 that SPICE
+MLR generated less residuals in most of observations.
In addition, the F-test of overall significance was con-
ducted to evaluate the multivariable linear regression models
and the PLSR model. F statistics and the corresponding P
values are shown in Table 2. All the Pvalues were less than
the significant level of 0.1%, indicating that the derived
models fit significantly better than a degenerate model with
no predictor variables.
3.5. Result Verification. The above models were first trained
by cross-validation and then evaluated on the testing set to
verify its effectiveness on validation and testing set. The
entire datasets were split into training, validation, and test-
ing folds. To be more specific, 48 samples under 12 condi-
tions (2 storage conditions over 6 time points, each
condition including 4 replicates) were randomly divided into
4 groups, one for testing and the other 3 for training and val-
idation. Each fold contained 12 samples; 1 replication ran-
domly selected from each condition. The training and
validation dataset were shuffled in every repetition.
The training process was conducted for 10 repetitions
over 3-fold. In each repetition, we trained the model by 3-
fold cross-validation and tested the trained model on the
testing fold, by calculating the mean and standard deviation
of the testing and training prediction error (Table 3). The
results shown in Figure 6 are generated by the model that
was selected according to the root means square error
(RMSE) and R-squared value from training and validation
folds. The primary observation from Table 3 is that the
SPICE+MLR and MI-ACE+MLR approaches outperformed
the PLSR model on the testing error, implying less overfit-
ting. Note that the better performance of the testing data
(as compared to the training set) is explained in the discus-
sion about sample outliers below.
3.6. Outliers. The smaller value of the prediction error for the
testing fold compared with training and validation folds in
Table 2 can be explained by the observed outliers
(Figure S1). The prediction performance of the training
fold using the SPICE method is plotted against the ground-
truth, with the marker size and color related to their
prediction error. Apparently, the three circled outliers in
each generated a greater amount of prediction error
MAM1 MAM3
ESM1
CYP79
FMOGSOX1
Aldoxime
Relative expression to Actin
Relative expression to Actin
MAM 1
STSB2
Methylsulfinylalkyl-
glucosinolate
2–OOB–6–MTH acid
MAM 3
5-OXO-5-MTP acid
2-OXO-4-MTB acidMethionine
Indole
glucosinolate
Alkyl
glucosinolate
ST5B2
AOP2
BCAT4
ESP25
ESM
CYP79
Day1 Day3
0
1
2
3
4
25°C 4°C
Day5 Day1 Day3 Day5
Day1 Day3
25°C 4°C
Day5 Day1 Day3 Day5 ESP25
FMO-GS OX1
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
⁎
Day1
0
14
24
34
44
100
200
0
10
20
30
300
400
Day3
25°C 4°C
Day5 Day1 Day3 Day5
Day1
0
5
10
15
100
200
300
0
5
10
20
15
30
25
300
400
0
10
20
40
120
80
160
200
0
10
20
30
150
200
Day3
25°C 4°C
Day5 Day1 Day3 Day5
Day1 Day3
25°C 4°C
Day5 Day1 Day3 Day5
Day1 Day3
25°C 4°C
Day5 Day1 Day3 Day5
Day1
0
2
4
6
Day3
25°C 4°C
AOP2
Day5 Day1 Day3 Day5
Day1 Day3
25°C 4°C
Day5 Day1 Day3 Day5
(a) (b)
(c)
(d)
(e)(g)
(h) (f)
Figure 4: Transcript levels of genes in the glucosinolate biosynthetic pathway during room temperature and cold storage: MAM1 (a),
MAM3 (b), ESM1 (c), CYP79 (d), ESP25 (e), FMO-GSOX1 (f), ST582 (g), and ADP2 (h). The transcript levels of each candidate gene are
reported as the relative expression to Actin from samples stored at 25
°
C (red) or 4
°
C (blue) and sampled on days 1, 3, and 5. The genes
encoding key enzymes are highlighted in yellow. The y-axis is the relative expression of each gene that was normalized using actin as an
internal control. Data represents means ± SE bars (n=3). The key enzymes were highlighted in yellow. Asterisks (∗) indicate statistically
significant differences from day 1 (control) to day 3 or day 5 (storage temperature conditions) (P<0:05).
8 Plant Phenomics

compared to the other data points. Table S1 lists the
glucosinolate concentrations of 4 replications stored in
room temperature at each of the 6 time points. The three
bold numbers correspond to the circled outliers in
Figure S1. In replicate 1, the glucosinolate concentration
was increased along days, while in replicates 2-4, the bold
numbers showed “abnormal”performance. An additional
experiment was conducted, where the outliers were moved
to the testing fold. Figure S2 and Table S2 show the
prediction performance and error of this additional testing
fold.
4. Discussion
Senescence is an important and fleeting state, and its onset
and progression after harvest cannot be easily detected.
The current lack of objective indices for defining tissue
senescence in fruit and vegetables limited our capacity to
control product quality and leads to food waste. However,
senescence is a physiological process initiated at or near har-
vest that can be tracked by monitoring well-described
changes in gene expression, physiological process, and meta-
bolomic signatures. In this work, we propose that glucosino-
lates are associated with discrete stages of senescence, for
potential use as diagnostic indicators of freshness. Using
HPLC and quantitative real-time PCR analysis, we evaluated
glucosinolate concentration and the expression of key genes
in glucosinolate biosynthetic pathway in postharvest broc-
coli. We found that there is a linear correlation between
the glucosinolate production and postharvest senescence in
broccoli. This lineage of glucosinolate could result from the
metabolic accumulation of glucosinolates during progres-
sion of senescence in stored broccoli. Our data determined
that transcriptional level of glucosinolate biosynthesis
increased rapidly when stored at room temperature. There-
fore, we demonstrated that glucosinolate content can be
used to detect the early senescence potentially serving as a
“freshness indicator”in broccoli and to define a freshness
signature when stored at room temperature.
We further developed the imaging analysis on the initia-
tion and progression of senescence in broccoli. We per-
formed two featured extraction approaches in estimating
the endmembers (a.k.a. signatures) of broccoli tissue over
storage period. The estimated abundance value via SPICE
0500 550 600 650 700
Wavelength (nm)
(a) (c) (e)
(b) (d) (f)
Estimated endmembers
EM1
EM2
EM3
750 800 850 900
0.02
0.04
0.06
0.08
Reflectance (%)
0.1
0.12
0.14
500 600 700
Wavelength (nm)
Discriminative target signature
Discriminative target signature
Background signature
800 900
0
0.05
0.1
Reflectance (%)
0.15
0.2
0.25
–0.05
–0.1
–0.15
1
0.8
D1
EM1
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
EM2 EM3
D5
D12
0.6
0.4
0.2
0
0.5
D1D5D12
0
00.5
EM1
(13)
1
0.5
0
00.5
EM2
(16)
1
0.5
0
00.5
EM3
(19)
1
0.2
0
00.5
EM1
(14)
1
0.1
0
00.5
EM2
(17)
1
0.1
0.2
0
00.5
EM3
(20)
1
0.1
0.2
0
00.5
EM1
(15)
1
0.2
0
00.5
EM2
(18)
1
0.2
0
00.5
EM3
(21)
1
0.2
D1
(10)
(11)
(12)
0.4
0.2
0
D5
D12
0.3 (22)
(23)
(24)
0.2
0.1
D1D5D12
0
0 0.1 0.2
Confidence value
Confidence value
Confidence value
0.3 0.4
0.3
0.2
0.1
0
0 0.1 0.2 0.3 0.4
0.3
0.2
0.1
0
0 0.1 0.2 0.3 0.4
Figure 5: Visualization of HSI analysis of testing samples on days 1, 5, and 12, (a, c-d) for SPICE and (b, e-f) for MI-ACE. (a) Estimated
endmembers using the SPICE methods on spectral images of broccoli florets. “EM”is an abbreviation of endmember. (b) Estimated
discriminative target and background signature using the MIACE methods. (c) Abundance map of estimated endmembers for testing
samples on day 1, day 5, and day 12. (d) Histogram of abundance value. The legend of x-axis and y-axis are the abundance value and
their proportion, respectively. (e) Confidence map of target detected by MI-ACE for testing samples on day 1, day 5, and day 12. (f)
Histogram of confidence value. The legend of x-axis and y-axis are the confidence value and their proportion, respectively.
9Plant Phenomics

and confidence value via MI-ACE approach not only visual-
ized the broccoli in different postharvest stage, but also indi-
cated the changes in glucosinolate concentration values. The
prediction error was explained by the fact that the measure-
ments of the hyperspectral data and the glucosinolate con-
centrations were conducted across different sample scales.
Namely, the abundance values are derived from imaging
across the entire surface of one side of a broccoli sample,
whereas the glucosinolate value was measured using only
one small component of the broccoli tissue. The RMSE
values showed that unmixing the broccoli florets only has
slightly less error than using the entire broccoli sample.
Although the RMSE values of MI-ACE did not show much
improvement compared with unmixing methods, the esti-
mated discriminative target signature was easier to interpret,
implying the specific wavelength where the broccoli spectra
were affected by increasing glucosinolates concentrates. In
addition, we compared the prediction result with the PLSR
042121086
Observation
(a) (b)
–80
–60
–40
–20
20
Residual
40
60 Entire broccoli
SPICE + MLR
MI-ACE + MLR
PLSR
Broccoli floret
0
042610
812
Observation
–100
–60
–20
0
60
Residual
80
100
40
–80
–40
20
Figure 6: Residuals of predicted glucosinolate levels on testing fold. (a) Residuals on the entire broccoli. (b) Residuals on the broccoli florets.
The x-axis indicates the observations. The y-axis indicates the residuals that subtracted the predicted values from the measured values.
Markers in various colors denote the glucosinolate levels predicted by the different methods. Markers that are closer to 0 are more
accurate predictions.
Table 2: Significant test for multivariable linear regression model.
SPICE+MLR MI-ACE+MLR PLSR
F statistics Pvalue F statistics Pvalue F statistics Pvalue
Entire broccoli 23.12 4.93e-6 124.96 1.53e-10 15.42 4.28e-8
Broccoli florets 51.24 8.34e-9 115.35 3.24e-10 15.48 4.11e-8
Table 3: The error of prediction of glucosinolate levels using MLR model with abundances calculated by SPICE, confidence calculated by
MI-ACE, as well as the PLSR model.
SPICE + MLR MI-ACE + MLR PLSR
RMSE R
2
RMSE R
2
RMSE R
2
Entire Broccoli Training and validation 44:93 ± 1:37 0:72 ± 0:02 46:14 ± 3:12 0:71 ± 0:04 42:94 ± 1:09 0:74 ± 0:01
Testing 26:14 ± 4:87 0:85 ± 0:06 30:21 ± 9:70 0:81 ± 0:11 31:00 ± 2:61 0:80 ± 0:03
Broccoli Florets Training and validation 44:78 ± 1:23 0:72 ± 0:02 47:94 ± 1:93 0:68 ± 0:03 45:97 ± 8:74 0:70 ± 0:12
Testing 21:34 ± 3:26 0:90 ± 0:03 23:67 ± 2:99 0:88 ± 0:03 36:69 ± 7:92 0:70 ± 0:12
10 Plant Phenomics

approach and showed that our feature extraction + MLR
model were more stable and less overfitting.
In summary, hyperspectral imaging held promising
strength in demonstrating state-of-the-art performance in
crop sciences through the modulation of imaging with spec-
troscopy. As shown in this effort, HSI had the potential to
provide quantitative parameters in detecting the content of
glucosinolates that associated with postharvest senescence.
The accumulation of glucosinolates marks the beginning of
senescence before it is visible in postharvest broccoli. Our
results can be directly extended to the other fresh crucifers
such as cabbage, kale, and cauliflowers as well as the other
fresh produce. The outcomes of the results will provide
insights into early detection of deterioriation of fruit and
vegetables throughout the food production pipeline, there-
fore understand how food waste can be reduced.
Data Availability
The data used to support the findings of this study are
included within the article.
Additional Points
No seed has been collected. The plant materials were har-
vested from the local farm followed by the permissions and
licenses of Florida agricultural guideline. The experimental
research on plants was complied with institutional, national,
and international guidelines and legislation.
Conflicts of Interest
The authors declare no competing and no potential conflict
of interest.
Authors’Contributions
T.L. and A.Z. designed the research. X.G. and Y.A. per-
formed the hyperspectral imaging. X.G. and A.Z. conducted
imaging data processing and algorithm development. Y.A.
prepared samples for HPLC analysis. Y.A. conducted qPCR
experiment and data analysis. T.L. A.Z. X.G., and Y.A. pre-
pared the figures and wrote the manuscript. Xiaolei Guo
and Yogesh K. Ahlawat contributed equally to this work.
Acknowledgments
We thank Dr. Diane Rowland for providing the HinaLea
4200 hyperspectral camera and for support in sample imag-
ing. We thank Dr. Ru Dai and Dr. Jeongim Kim for the
HPLC analysis and troubleshooting. This work was sup-
ported by UF Seed Fund (#P0175583 to A.Z. and T.L.) and
USDA-NIFA GRANT13169257.
Supplementary Materials
Figure S1: predicted glucosinolate levels in training folds.
The x-axis indicated the real glucosinolate concentration,
and the y-axis indicated the predicted values. Markers that
are closer to the “x=y”line are more accurate predictions.
The marker size and color corresponded to the prediction
error; the bigger and brighter markers indicated greater
error. Figure S2: residuals of predicted glucosinolate levels
on additional testing fold. (a) Residuals on the entire broc-
coli. (b) Residuals on the broccoli florets. The x-axis indi-
cates the observations. The y-axis indicates the residuals
that subtracted the predicted values from the measured
values. Markers in various colors denote the glucosinolate
levels predicted by the different methods. Markers that are
closer to 0 are more accurate predictions. Figure S3: explora-
tion of SPICE parameters. (a-e) The training and validation
errors across various parameter settings for the entire broc-
coli, in replicate. (f-j) The training and validation errors
across various parameter settings for broccoli florets. Specif-
ically, (a) and (f) measure the RMSE over Γ, (b) and (g)
measure the R2 over Γ, (c) and (h) measure the RMSE over
M, (d) and (i) measure the R2 over M, and (e) and (j) show
the histogram of M over all replications. Table S1: the gluco-
sinolate concentration under 25
°
C on each sampling day.
Table S2: comparison of prediction error on additional test-
ing fold. (Supplementary Materials)
References
[1] D. Angelino and E. Jeffery, “Glucosinolate hydrolysis and bio-
availability of resulting isothiocyanates: focus on glucorapha-
nin,”Journal of Functional Foods, vol. 7, pp. 67–76, 2014.
[2] R. E. Schouten, X. Zhang, R. Verkerk et al., “Modelling the
level of the major glucosinolates in broccoli as affected by con-
trolled atmosphere and temperature,”Postharvest Biology and
Technology, vol. 53, no. 1-2, pp. 1–10, 2009.
[3] V. Casajús, P. Demkura, P. Civello, M. G. Lobato, and
G. Martínez, “Harvesting at different time-points of day affects
glucosinolate metabolism during postharvest storage of broc-
coli,”Food Research International, vol. 136, article 109529,
2020.
[4] D. Villarreal-García, V. Nair, L. Cisneros-Zevallos, and D. A.
Jacobo-Velázquez, “Plants as biofactories: postharvest stress-
induced accumulation of phenolic compounds and glucosino-
lates in broccoli subjected to wounding stress and exogenous
phytohormones,”Frontiers in Plant Science, vol. 7, 2016.
[5] Y.-Z. Feng and D.-W. Sun, “Application of hyperspectral
imaging in food safety inspection and control: a review,”Crit-
ical Reviews in Food Science and Nutrition, vol. 52, no. 11,
pp. 1039–1058, 2012.
[6] A. Gowen, C. Odonnell, P. Cullen, G. Downey, and J. Frias,
“Hyperspectral imaging - an emerging process analytical tool
for food quality and safety control,”Trends in Food Science
& Technology, vol. 18, no. 12, pp. 590–598, 2007.
[7] R. L. Shewfelt, E. K. Heaton, and K. M. Batal, “Nondestructive
color measurement of fresh broccoli,”Journal of Food Science,
vol. 49, no. 6, pp. 1612-1613, 1984.
[8] C. B. Singh, D. S. Jayas, J. Paliwal, and N. D. G. White, “Fungal
damage detection in wheat using short-wave near-infrared
hyperspectral and digital colour imaging,”International Jour-
nal of Food Properties, vol. 15, no. 1, pp. 11–24, 2012.
[9] S. Medeiros, M. Lucimar, J. P. da Cruz-Tirado et al., “Assess-
ment oil composition and species discrimination of _Brassi-
cas_ seeds based on hyperspectral imaging and portable near
infrared (NIR) spectroscopy tools and chemometrics,”Journal
11Plant Phenomics

of Food Composition and Analysis, vol. 107, article 104403,
2022.
[10] K. D. Singh, H. S. N. Duddu, S. Vail, I. Parkin, and S. J. Shir-
tliffe, “UAV-based hyperspectral imaging technique to esti-
mate canola (Brassica napus L.) seedpods maturity,”
Canadian Journal of Remote Sensing, vol. 47, no. 1, pp. 33–
47, 2021.
[11] X. Yu, L. Huanda, and Q. Liu, “Deep-learning-based regres-
sion model and hyperspectral imaging for rapid detection of
nitrogen concentration in oilseed rape (Brassica napus L.)
leaf,”Chemometrics and Intelligent Laboratory Systems,
vol. 172, pp. 188–193, 2018.
[12] P. M. Mehl, K. Chao, M. Kim, and Y. R. Chen, “Detection of
defects on selected apple cultivars using hyperspectral and
multispectral image analysis,”Applied Engineering in Agricul-
ture, vol. 18, no. 2, 2002.
[13] X. Cheng, Y. R. Chen, Y. Tao, C. Y. Wang, M. S. Kim, and
A. M. Lefcourt, “A novel integrated PCA and FLD method
on hyperspectral image feature extraction for cucumber chill-
ing damage inspection,”Transactions of the ASAE, vol. 47,
no. 4, pp. 1313–1320, 2004.
[14] F. Mendoza, L. Renfu, and H. Cen, “Comparison and fusion of
four nondestructive sensors for predicting apple fruit firmness
and soluble solids content,”Postharvest Biology and Technol-
ogy, vol. 73, pp. 89–98, 2012.
[15] L. Feng, M. Zhang, B. Adhikari, and Z. Guo, “Nondestruc-
tive detection of postharvest quality of cherry tomatoes
using a portable NIR spectrometer and chemometric algo-
rithms,”Food Analytical Methods, vol. 12, no. 4, pp. 914–
925, 2019.
[16] A. Rady, N. Ekramirad, A. A. Adedeji, M. Li, and
R. Alimardani, “Hyperspectral imaging for detection of cod-
ling moth infestation in GoldRush apples,”Postharvest Biology
and Technology, vol. 129, pp. 37–44, 2017.
[17] P. Rajkumar, N. Wang, G. EImasry, G. S. V. Raghavan, and
Y. Gariepy, “Studies on banana fruit quality and maturity
stages using hyperspectral imaging,”Journal of Food Engineer-
ing, vol. 108, no. 1, pp. 194–200, 2012.
[18] G. ElMasry, N. Wang, and C. Vigneault, “Detecting chilling
injury in red delicious apple using hyperspectral imaging and
neural networks,”Postharvest Biology and Technology,
vol. 52, no. 1, pp. 1–8, 2009.
[19] S. Zou, Y.-C. Tseng, A. Zare, D. L. Rowland, B. L. Tillman, and
S.-C. Yoon, “Peanut maturity classification using hyperspec-
tral imagery,”Biosystems Engineering, vol. 188, pp. 165–177,
2019.
[20] Y.-R. Chen, K. Chao, and M. S. Kim, “Machine vision technol-
ogy for agricultural applications,”Computers and Electronics
in Agriculture, vol. 36, no. 2-3, pp. 173–191, 2002.
[21] J. G. Lee, S. Lim, J. Kim, and E. J. Lee, “The mechanism of dete-
rioration of the glucosinolate-myrosynase system in radish
roots during cold storage after harvest,”Food Chemistry,
vol. 233, pp. 60–68, 2017.
[22] A. Zare and P. Gader, “Sparsity promoting iterated con-
strained endmember detection in hyperspectral imagery,”
IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 3,
pp. 446–450, 2007.
[23] A. Zare, C. Jiao, and T. Glenn, Multiple instance discriminative
target characterization, Code Ocean, 2017.
[24] A. Zare, C. Jiao, and T. Glenn, “Discriminative multiple
instance hyperspectral target characterization,”IEEE Transac-
tions on Pattern Analysis and Machine Intelligence, vol. 40,
no. 10, pp. 2342–2354, 2018.
[25] A. Alizadeh Naeini, M. Babadi, and S. Homayouni, “Assess-
ment of normalization techniques on the accuracy of hyper-
spectral data clustering,”The International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sci-
ences, vol. XLII-4/W4, pp. 27–30, 2017.
[26] E. W. Weisstein, “Norm,”https://mathworld.wolfram.com/.
2002 March.
[27] V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, “Graph-
cut textures,”ACM Transactions on Graphics, vol. 22, no. 3,
pp. 277–286, 2003.
[28] A. Gilat, MATLAB: An Introduction with Applications (Vol. 3),
Wiley, New York, 2008.
[29] N. Keshava and J. F. Mustard, “Spectral unmixing,”IEEE Sig-
nal Processing Magazine,vol. 19, no. 1, pp. 44–57, 2002.
[30] S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a
basic tool of chemometrics,”Chemometrics and Intelligent
Laboratory Systems, vol. 58, no. 2, pp. 109–130, 2001.
12 Plant Phenomics