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Journal ArticleDOI

Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli

01 Apr 2014-Journal of Food Engineering (Elsevier)-Vol. 126, pp 107-112

AbstractThe use of hyperspectral imaging to (a) quantify and (b) localise total glucosinolates in florets of a single broccoli species has been examined Two different spectral regions (vis–NIR and NIR), a number of spectral pre-treatments and different mask development strategies were studied to develop the quantitative models These models were then applied to freeze-dried slices of broccoli to identify regions within individual florets which were rich in glucosinolates The procedure demonstrates potential for the quantitative screening and localisation of total glucosinolates in broccoli using the 950–1650 nm wavelength range These compounds were mainly located in the external part of florets

Topics: Hyperspectral imaging (55%)

Summary (2 min read)

1. Introduction

  • The aim of this study was to evaluate the potential of hyperspectral imaging technology for the quantitative screening and localisation of total glucosinolates in freeze-dried broccoli.
  • Since predictive models developed on freeze-dried powders by conventional NIR spectrometers may not be transferred directly to hyperspectral imaging datasets, a new predictive model must be generated using an actual hyperspectral imaging system on homogeneous, freeze-dried broccoli powders after which it may be applied to hyperspectral images of intact broccoli for localisation and quantitation of total glucosinolates.
  • To their knowledge, this is the first time that this analytical tool has been applied to broccoli for these purposes.

2.2. Hyperspectral imaging analysis

  • Data were recorded in units of reflectance and saved in ENVI header format using the instrument acquisition software (Spectral Scanner; DV Optics, Padua, Italy).
  • When using System 1, only spectral data in the 450 -900 nm regions were used in data analysis due to reduced efficiency of the light source and CCD in wavelength regions outside this range.
  • In the case of System 2, the spectral range was attenuated to 950 -1650 nm for similar operational reasons.

2.3. Data processing and analysis

  • Data treatment and quantitative model development was carried out using Matlab (R2010b; The Math Works, Inc. USA).
  • For each hyperspectral image, regions of interest (ROIs) of approx.
  • 3 cm diameter were selected using an interactive selection tool available in the acquisition software ('ROI tool') and 500 pixels were randomly-selected within each ROI.
  • Spectral data were pre-treated using the standard normal variate (SNV) transform to diminish the effects of light scatter.
  • Finally, quantitative calibrations were developed by partial least squares (PLS) regression using total glucosinolates as the dependent (Y) variable and pixel spectra as the independent (X) variables.

2.4 Prediction map

  • Slices of whole freeze-dried broccoli were scanned in the NIR zone (950-1650 nm) to apply the previously constructed model and identify the glucosinolate allocation.
  • Prior to the quantitative analysis, a thresholding rule method was applied to the broccoli images to isolate the broccoli from other parts of image.
  • An image was generated using the maximum reflectance value of each pixel spectrum in a raw image.
  • A threshold of 0.45 reflectance units was set analysing the corresponding histogram and drawing a tentative mask image in an iterative process.
  • SNV was applied to minimise the effects of scattering in the mask created and then the PLS model was applied.

3.2. Prediction map

  • Their potent odour and pronounced taste suggests a role in herbivore and microbial defence.
  • Deposition in external plant parts, confirmed spectroscopically in this work, would be the optimal location for these purposes.

4. Conclusion

  • Two different spectral regions (vis-NIR and NIR) were studied to develop the quantitative models.
  • Better results were obtained using the 950-1650 nm wavelength range and subsequent analyses were therefore carried out using this spectral zone.
  • The procedure demonstrates potential for the quantitative screening and location of total glucosinolates in broccoli using the 950-1650 nm wavelength range.
  • Nevertheless, a comprehensive study should be made in order to evaluate all other relevant sources of variability in the complete development of these models.
  • Such a study would entail several years work but the results reported herein suggest the viability of obtaining useful results from such an undertaking.

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Preliminary study on the use of near infrared hyperspectral imaging for quantitation and
localisation of total glucosinolates in freeze
-
dried broccoli
José Miguel Hernández
-
Hierro, Carlos Esquerre, Juan Valverde, Salvador Villacreces,
Kim Reilly, Mich
-
P. O’Donnell, Gerard Downey
This article is provided by the author(s) and Teagasc T
-
Stór in accordance with
publish
er policies.
Please cite the published version.
The correct citation is available in the
T
-
Stór
record for this article.
This item is made available to you under the Creative Commons Attribution
-
Non
commercial
-
No Derivatives 3
.0 License.
NOTICE: This is the author’s version of a work that was accepted for publication in
Journal of Food Engineering
. Changes resulting f
rom the publishing process, such as
peer review, editing, corrections, structural formatting, and other quality control
mechanisms may not be reflected in this document. Changes may have been made to
this work since it was submitted for publication. A defi
nitive version was
subsequently published in
Journal of Food Engineering, Volume 126, April 2014,
Pages 107
-
112.
DOI 10.1016/j.jfoodeng.2013.11.005.

Preliminary study on the use of
near infrared
hyperspectral imaging
for
quantitation
and
localisation
of total glucosinolates in
freeze
-
dried
brocc
oli
José Miguel Hernández
-
Hierro
1
, Carlos Esquerre
2
, Juan Valverde
3
, Salvador
Villacreces
3
, Kim Reilly
4
, Michael Gaffney
4
, M. Lourdes González
-
Miret
1
, Francisco J.
Heredia
1
, Colm P. O’Donnell
2
, Gerard Downey
3,*
1
Food Colour & Quality Laboratory, Departmen
t of Nutrition & Food Science,
Universidad de Sevilla, Facultad de Farmacia, 41012 Sevilla, Spain.
2
Biosystems Engineering, School of Agriculture, Food Science and Veterinary
Medicine
,
University College Dublin, Belfield, Dublin 4, Ireland.
3
Teagasc Food R
esearch Centre
Ashtown
, Dublin 15, Dublin, Ireland.
4
Horticulture Development Unit, Teagasc Research Centre, Kinsealy, Dublin 17,
Ireland.
* Corresponding author:
Gerard Downey
Phone:
(+353 1) 805 9500
Fax:
(+353 1) 805 9550
E
-
mail:
g
erard.
d
owney@teagasc.
ie

Abstract
1
T
he use of hyperspectral imaging to
(a) quantify and (b)
loca
lise
total
glucosinolates
in
2
florets of
a single
broccoli
species has been examined
. Two different spectral re
gions
3
(
v
is
-
NIR and NIR)
, a
number
of spectral pre
-
treatments and differe
nt mask development
4
strategies were
studied
to develop the quantitative models.
These models were then
5
applied to freeze
-
dried slices of broccoli to identify regions
within individual florets
6
which were
rich in glucosinolates.
The procedure demonstrates po
tential for the
7
quantitative screening and loca
lisa
tion of total glucosinolates in broccoli using the 950
-
8
1650 nm wavelength range.
T
hese compounds were mainly located in the external part
9
of florets.
10
Keywords:
Glucosinolates, broccoli,
hyperspectral imagi
ng
,
near infrared, visible,
11
chemometrics.
12

1. Introduction
13
Glucosinolates are a class of about 120 chemicals distributed in only 16 plant families.
14
The
se
compounds are well
-
known for their characteristic pungent smells and tastes
15
which are typical of
some
Brassica
vegetables such as cabbage, mustard, cress,
16
cauliflower, broccoli, turnip,
B
russel sprouts, radish and horseradish.
Structurally,
17
glucosinolates (β
-
thioglucoside
-
N
-
hydroxysulfates) are characterised by the presence of
18
nitrogen and sulphur groups
.
Biosynthesis of
glucosinolates
is
mainly
carried out using
19
glucose and amino acid
s such as methionine, alanine, leucine and v
aline
(aliphatic
20
glucosinolates) or
tryptophan and phenylalanine (aromatic glucosinolates)
(Crozier et
21
al., 2006)
.
Epidemiological studies have consistently reported a reduc
ed
incidence of a
22
n
umber of diseases
in subjects consuming
diets rich in these compounds
although anti
-
23
nutritive effects of both glucosinolates and
their
hydrolysis products have also been
24
reported
(Crozier et al., 2006
; Jeffery and Araya, 2009; Shahindi, 1990; Verkerk et al.,
25
2009)
.
Broccoli (
Brassica oleracea
L. var
Italica
)
contains significant amounts of
these
26
potential
ly
bioactive compounds
(Vallejo et al.,
2003; Wang et al., 2012a)
.
This
27
vegetable
is an economically important crop in
a number of
countries
which may act
as
28
a
source
not only of glucosinolates but also of
vitamins, minerals and
other beneficial
29
phytochemicals
(Jeffery et al., 2003; Wang et al., 2012a)
. It is therefore important to
30
both
characterise the content of bioactive compounds
in broccoli
and determine in what
31
parts of the plant the
se
bioactive compounds are accumulated.
Our previous work has
32
demonstrate
d the potential for near infrared spectroscopy to quantify total
33
glucosinolates in freeze
-
dried powders with acceptable accuracy for screening
purposes
34
(Hernandez
-
Hierro et al., 2012)
.
Information about the spatial distribution of the
35
aforementioned compounds
would
also
be
useful
but near infrared spectroscopy do
es
36

not provi
de
the capability to map the location of constituents
. Hyperspectral imaging
37
may hold the answer to this problem
.
38
Hyperspectral imaging is an emerging technique for non
-
destructive food analysis
39
which provides both spatial and spectral information
about
an
object
.
Recorde
d
images
40
consist of many thousands of pixels in a two
-
dimensional array, with each pixel
41
corresponding to a specific region on the surface of the sample
;
e
ach pixel in
a
42
hyperspectral image
therefore
contains
a
spectrum of
the sample at
tha
t specific
43
position
.
Interrogation of these spectra makes possible the development of mathematical
44
models to predict the chemical composition or functional class of a sample at each
45
pixel.
Reflectance imaging is the most common
image acquisition
mode and i
s usually
46
carried out in
either
the visible
-
near infrared
(vis
-
NIR
;
400
-
1000 nm) or near infrared
47
(NIR
;
1000
-
1700 nm) spectral regions
(Gowen et al., 2007)
.
The use of multivariate
48
chemometric methods is required
to
handl
e
the
large quantities of spectral data collected
49
in each image
and very many
approaches are available for the development of
50
regression models
to
predict constitu
ent
concentrations in a sample
at pixel level.
51
The
number of research
applications
of
hyperspectral analysis
has
risen
considerably in
52
the
food sector
in
the
recent
past
(Burger and Gowen, 2011; Gowen et al., 2007;
53
Lorente et al., 2012; McGoverin et al., 2010; Sun, 2010)
.
H
yperspectral
image
analysis
54
has been used to determine moisture, total soluble solid
s and pH in strawberries
55
(ElMasry et al., 2007)
,
firmness
and soluble solids
in apples
(Mendoza et al., 2011;
56
Wang et al., 2012b)
anthocyanins in grape skins
(Fernandes et al., 2011)
,
chlorophyll
57
distribution in cucumber lea
ves
(Ji
-
Yong et al., 2012)
and maturity stag
e of bananas
58
(Rajkumar et al., 2012)
.
Additionally
, this
analytical
method has been used to determine
59
moisture in
dehydrated
prawns
(Wu et al., 2012)
and some
qua
lity
parameters
of
both
60
lamb
(Kamruzzaman et al., 2012)
and pork
(Barbin et al., 2012)
meat
.
R
esults reported
61

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References
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Book
30 Jul 2004
Abstract: Series Preface.Preface.Acronyms, Abbreviations and Symbols.About the Author.1. Introduction.1.1 Electromagnetic Radiation.1.2 Infrared Absorptions.1.3 Normal Modes of Vibration.1.4 Complicating Factors.1.4.1 Overtone and Combination Bands.1.4.2 Fermi Resonance.1.4.3 Coupling.1.4.4 Vibration-Rotation Bands.References.2. Experimental Methods.2.1 Introduction.2.2 Dispersive Infrared Spectrometers.2.3 Fourier-Transform Infrared Spectrometers.2.3.1 Michelson Interferometers.2.3.2 Sources and Detectors.2.3.3 Fourier-Transformation.2.3.4 Moving Mirrors.2.3.5 Signal-Averaging.2.3.6 Advantages.2.3.7 Computers.2.3.8 Spectra.2.4 Transmission Methods.2.4.1 Liquids and Solutions.2.4.2 Solids.2.4.3 Gases.2.4.4 Pathlength Calibration.2.5 Reflectance Methods.2.5.1 Attenuated Total Reflectance Spectroscopy.2.5.2 Specular Reflectance Spectroscopy.2.5.3 Diffuse Reflectance Spectroscopy.2.5.4 Photoacoustic Spectroscopy.2.6 Microsampling Methods.2.7 Chromatography-Infrared Spectroscopy.2.8 Thermal Analysis-Infrared Spectroscopy.2.9 Other Techniques.References.3. Spectral Analysis.3.1 Introduction.3.2 Group Frequencies.3.2.1 Mid-Infrared Region.3.2.2 Near-Infrared Region.3.2.3 Far-Infrared Region.3.3 Identification.3.4 Hydrogen Bonding.3.5 Spectrum Manipulation.3.5.1 Baseline Correction.3.5.2 Smoothing.3.5.3 Difference Spectra.3.5.4 Derivatives.3.5.5 Deconvolution.3.5.6 Curve-Fitting.3.6 Concentration.3.7 Simple Quantitative Analysis.3.7.1 Analysis of Liquid Samples.3.7.2 Analysis of Solid Samples.3.8 Multi-Component Analysis.3.9 Calibration Methods.References.4. Organic Molecules.4.1 Introduction.4.2 Aliphatic Hydrocarbons.4.3 Aromatic Compounds.4.4 Oxygen-Containing Compounds.4.4.1 Alcohols and Phenols.4.4.2 Ethers.4.4.3 Aldehydes and Ketones.4.4.4 Esters.4.4.5 Carboxylic Acids and Anhydrides.4.5 Nitrogen-Containing Compounds.4.5.1 Amines.4.5.2 Amides.4.6 Halogen-Containing Compounds.4.7 Heterocyclic Compounds.4.8 Boron Compounds.4.9 Silicon Compounds.4.10 Phosphorus Compounds.4.11 Sulfur Compounds.4.12 Near-Infrared Spectra.4.13 Identification.References.5. Inorganic Molecules.5.1 Introduction.5.2 General Considerations.5.3 Normal Modes of Vibration.5.4 Coordination Compounds.5.5 Isomerism.5.6 Metal Carbonyls.5.7 Organometallic Compounds.5.8 Minerals.References.6. Polymers.6.1 Introduction.6.2 Identification.6.3 Polymerization.6.4 Structure.6.5 Surfaces.6.6 Degradation.References.7. Biological Applications.7.1 Introduction.7.2 Lipids.7.3 Proteins and Peptides.7.4 Nucleic Acids.7.5 Disease Diagnosis.7.6 Microbial Cells.7.7 Plants.7.8 Clinical Chemistry.References.8. Industrial and Environmental Applications.8.1 Introduction.8.2 Pharmaceutical Applications.8.3 Food Science.8.4 Agricultural Applications.8.5 Pulp and Paper Industries.8.6 Paint Industry.8.7 Environmental Applications.References.Responses to Self-Assessment Questions.Bibliography.Glossary of Terms.SI Units and Physical Constants.Periodic Table.Index.

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"Preliminary study on the use of nea..." refers background in this paper

  • ...Absorbance around 1110 nm 195 may reflect 2 overtone –OH stretching although a combination –S=O stretch has been 196 previously reported around 1020-1060 nm (Stuart, 2004) which may be relevant given 197 the occurrence of sulphur-containing volatile compounds in broccoli (Jacobsson, 198 Nielsen and Sjöholm, 2004)....

    [...]

  • ...=O stretch has been196 previously reported around 1020-1060 nm (Stuart, 2004) which may be relevant given197 the occurrence of sulphur-containing volatile compounds in broccoli (Jacobsson,198 Nielsen and Sjöholm, 2004)....

    [...]


Book ChapterDOI
Abstract: The accuracy of an estimate of a normally distributed quantity is judged by reference to its variance, or rather, to an estimate of the variance based on the available sample. In 1908 “Student” examined the ratio of the mean to the standard deviation of a sample.1 The distribution at which he arrived was obtained in a more rigorous manner in 1925 by R.A. Fisher,2 who at the same time showed how to extend the application of the distribution beyond the problem of the significance of means, which had been its original object, and applied it to examine regression coefficients and other quantities obtained by least squares, testing not only the deviation of a statistic from a hypothetical value but also the difference between two statistics.

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TL;DR: HSI equipment, image acquisition and processing are described; current limitations and likely future applications are discussed; and recent advances in the application of HSI to food safety and quality assessment are reviewed.
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"Preliminary study on the use of nea..." refers background in this paper

  • ...…to predict constituent concentrations in a sample at pixel level.51 The number of research applications of hyperspectral analysis has risen considerably in52 the food sector in the recent past (Burger and Gowen, 2011; Gowen et al., 2007;53 Lorente et al., 2012; McGoverin et al., 2010; Sun, 2010)....

    [...]

  • ...Reflectance imaging is the most common image acquisition mode and is usually46 carried out in either the visible-near infrared (vis-NIR; 400-1000 nm) or near infrared47 (NIR; 1000-1700 nm) spectral regions (Gowen et al., 2007)....

    [...]


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Abstract: Glucosinolates (GLSs) are found in Brassica vegetables. Examples of these sources include cabbage, Brussels sprouts, broccoli, cauliflower and various root vegetables (e.g. radish and turnip). A number of epidemiological studies have identified an inverse association between consumption of these vegetables and the risk of colon and rectal cancer. Animal studies have shown changes in enzyme activities and DNA damage resulting from consumption of Brassica vegetables or isothiocyanates, the breakdown products (BDP) of GLSs in the body. Mechanistic studies have begun to identify the ways in which the compounds may exert their protective action but the relevance of these studies to protective effects in the human alimentary tract is as yet unproven. In vitro studies with a number of specific isothiocyanates have suggested mechanisms that might be the basis of their chemoprotective effects. The concentration and composition of the GLSs in different plants, but also within a plant (e.g. in the seeds, roots or leaves), can vary greatly and also changes during plant development. Furthermore, the effects of various factors in the supply chain of Brassica vegetables including breeding, cultivation, storage and processing on intake and bioavailability of GLSs are extensively discussed in this paper.

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Abstract: Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

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"Preliminary study on the use of nea..." refers background in this paper

  • ...…to predict constituent concentrations in a sample at pixel level.51 The number of research applications of hyperspectral analysis has risen considerably in52 the food sector in the recent past (Burger and Gowen, 2011; Gowen et al., 2007;53 Lorente et al., 2012; McGoverin et al., 2010; Sun, 2010)....

    [...]


Frequently Asked Questions (1)
Q1. What are the contributions mentioned in the paper "Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli" ?

1 The use of hyperspectral imaging to ( a ) quantify and ( b ) localise total glucosinolates in 2 florets of a single broccoli species has been examined. Two different spectral regions 3 ( vis-NIR and NIR ), a number of spectral pre-treatments and different mask development 4 strategies were studied to develop the quantitative models. The procedure demonstrates potential for the 7 quantitative screening and localisation of total glucosinolates in broccoli using the 9508 1650 nm wavelength range.