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Journal Article•DOI•

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

José Miguel Hernández-Hierro1, Carlos Esquerre2, Juan Valverde3, Salvador Villacreces3, Kim Reilly3, Michael Gaffney3, M. Lourdes González-Miret1, Francisco J. Heredia1, Colm P. O'Donnell2, Gerard Downey3 •
University of Seville1, University College Dublin2, Teagasc3
01 Apr 2014-Journal of Food Engineering (Elsevier)-Vol. 126, pp 107-112
TL;DR: In this article, the use of hyperspectral imaging to quantify and 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.
About: This article is published in Journal of Food Engineering.The article was published on 2014-04-01 and is currently open access. It has received 28 citations till now. The article focuses on the topics: Hyperspectral imaging.

Summary (2 min read)

Jump to: [1. Introduction] – [2.2. Hyperspectral imaging analysis] – [2.3. Data processing and analysis] – [2.4 Prediction map] – [3.2. Prediction map] and [4. Conclusion]

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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Citations
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Journal Article•DOI•
Classification of organic beef freshness using VNIR hyperspectral imaging.

[...]

Stuart Crichton1, Sascha Kirchner1, Victoria Porley2, Stefanie Retz1, Gardis von Gersdorff1, Oliver Hensel1, Martin Weygandt3, Barbara Sturm1, Barbara Sturm4 •
University of Kassel1, University of St Andrews2, Charité3, Newcastle University4
01 Jul 2017-Meat Science
TL;DR: Two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements are illustrated.

35 citations

Cites background from "Preliminary study on the use of nea..."

  • ...Most authors have focused on the use of relative reflectance spectra (Gowen, O’Donnell, Cullen, Downey, & Frias, 2007; He & Sun, 2015; Hernández-Hierro et al., 2014; Liu et al., 2014; Y.-Y. Pu & Sun, 2015; Wu et al., 2013; Wu, Sun, & He, 2012)....

    [...]

Journal Article•DOI•
Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

[...]

Eva M. Achata1, Elena S. Inguglia2, Carlos Esquerre1, Brijesh K. Tiwari2, Colm P. O'Donnell1 •
University College Dublin1, Teagasc2
01 Apr 2019-Journal of Food Engineering
TL;DR: In this paper, partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models for prediction of brining salt concentration employed.

34 citations

Journal Article•DOI•
Applications of Photonics in Agriculture Sector: A Review

[...]

Tan Jin Yeong1, Ker Pin Jern1, Lau Kuen Yao1, Mahammad A. Hannan1, Shirley Tang Gee Hoon2 •
Universiti Tenaga Nasional1, Management and Science University2
27 May 2019-Molecules
TL;DR: The spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization, and will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.
Abstract: The agricultural industry has made a tremendous contribution to the foundations of civilization. Basic essentials such as food, beverages, clothes and domestic materials are enriched by the agricultural industry. However, the traditional method in agriculture cultivation is labor-intensive and inadequate to meet the accelerating nature of human demands. This scenario raises the need to explore state-of-the-art crop cultivation and harvesting technologies. In this regard, optics and photonics technologies have proven to be effective solutions. This paper aims to present a comprehensive review of three photonic techniques, namely imaging, spectroscopy and spectral imaging, in a comparative manner for agriculture applications. Essentially, the spectral imaging technique is a robust solution which combines the benefits of both imaging and spectroscopy but faces the risk of underutilization. This review also comprehends the practicality of all three techniques by presenting existing examples in agricultural applications. Furthermore, the potential of these techniques is reviewed and critiqued by looking into agricultural activities involving palm oil, rubber, and agro-food crops. All the possible issues and challenges in implementing the photonic techniques in agriculture are given prominence with a few selective recommendations. The highlighted insights in this review will hopefully lead to an increased effort in the development of photonics applications for the future agricultural industry.

34 citations

Journal Article•DOI•
Use of hyperspectral imaging for the prediction of moisture content and chromaticity of raw and pretreated apple slices during convection drying

[...]

Stuart Crichton1, Luna Shrestha1, Anya Hurlbert2, Barbara Sturm1•
University of Kassel1, Newcastle University2
19 May 2018-Drying Technology
TL;DR: In this article, the feasibility of using spectral reflectance information in the visible-near infrared (400-1,000) region to estimate moisture content (gW/gDM) and chromaticity (CIELAB) of apple slices was investigated during convection drying.
Abstract: The feasibility of using spectral reflectance information in the visible—near infrared (400–1,000 nm) region to estimate moisture content (gW/gDM) and chromaticity (CIELAB) of apple slices was investigated during convection drying. Apple slices were pretreated with hot water blanching (50 and 70°C), acid application (citric and ascorbic), and combinations thereof before drying at 50 and 70°C. Prediction models for the space-averaged spectral reflectance curves were built using the partial least square regression method. A three-component partial least square regression (PLSR) model satisfied the minimal root mean square error (RMSE) criterion for predicting moisture content (avg. RMSEP = 0.13, r2 = 0.99); importantly, the critical wavelengths remained the same across all pretreatments (540, 817, 977 nm). Similarly, PLSR modeling showed that the optimal set of wavelengths (in terms of RMSE) were invariant across pretreatment for CIELAB a* prediction (543, 966 nm) and CIELAB b* prediction (510, 664, 714, 914, 969 nm). The stability of the information content of these wavelengths across pretreatments indicates their independence of color changes. Additionally, the spatial information in the hyperspectral images was exploited to visualize the performance of the predictive models by pseudo-coloring their values for each pixel in a single apple slice across different drying times. This visualization of spatial distribution of predicted moisture content and chromaticity changes shows significant potential for use in online monitoring of the drying process.

25 citations

Journal Article•DOI•
Segmentation and calibration of hyperspectral imaging for honey analysis

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Ary Noviyanto1, Waleed H. Abdulla1•
University of Auckland1
01 Apr 2019-Computers and Electronics in Agriculture
TL;DR: The proposed segmentation and calibration techniques ensure the repeatability of spectral information acquisition which is very important for further processing to develop machine learning and statistically based prediction models.

23 citations

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Barbara H. Stuart
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TL;DR: In this paper, the authors present a set of techniques for detecting anomalous infrared spectra, including Fourier Transform Infrared Spectrometers (FTIS) and Spectral Spectral Transform Transform (STT) this paper.
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2,802 citations

"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)....

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  • ...=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)....

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Book Chapter•DOI•
The Generalization of Student’s Ratio

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Harold Hotelling1•
Columbia University1
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TL;DR: In this article, the distribution at which Student arrived was obtained in a more rigorous manner in 1925 by R.A. Fisher, who at the same time showed how to extend the application of the distribution beyond the problem of 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.
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.

1,472 citations

Journal Article•DOI•
Hyperspectral imaging – an emerging process analytical tool for food quality and safety control

[...]

Aoife Gowen1, Colm P. O'Donnell1, Patrick J. Cullen2, Gerard Downey3, Jesus M. Frias2 •
University College Dublin1, Dublin Institute of Technology2, Teagasc3
01 Dec 2007-Trends in Food Science and Technology
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.
Abstract: Hyperspectral imaging (HSI) is an emerging platform technology that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool for non-destructive food analysis. This paper provides an introduction to hyperspectral imaging: HSI equipment, image acquisition and processing are described; current limitations and likely future applications are discussed. In addition, recent advances in the application of HSI to food safety and quality assessment are reviewed, such as contaminant detection, defect identification, constituent analysis and quality evaluation.

1,208 citations

"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)....

    [...]

Journal Article•DOI•
Glucosinolates in Brassica Vegetables - the Influence of the Food Supply Chain on Intake, Bioavailability and Human Health

[...]

Ruud Verkerk1, Monika Schreiner2, Angelika Krumbein2, Ewa Ciska3, Birgit Holst4, Ian Rowland5, Remi De Schrijver6, Magnor Hansen7, Clarissa Gerhäuser8, Richard Mithen9, Matthijs Dekker1 •
Wageningen University and Research Centre1, Leibniz Association2, Polish Academy of Sciences3, Nestlé4, University of Reading5, Catholic University of Leuven6, Norwegian University of Life Sciences7, German Cancer Research Center8, Norwich Research Park9
01 Sep 2009-Molecular Nutrition & Food Research
TL;DR: 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 article.
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.

531 citations

Journal Article•DOI•
Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

[...]

D. Lorente, Nuria Aleixos1, Juan Gómez-Sanchis2, Sergio Cubero, Oscar Leonardo García-Navarrete3, José Blasco •
Polytechnic University of Valencia1, University of Valencia2, National University of Colombia3
01 May 2012-Food and Bioprocess Technology
TL;DR: 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 are explained, with details of the statistical techniques most commonly used for this task.
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.

444 citations

"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)....

    [...]

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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.