Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging

Ariana, Diwan P.; Lu, Renfu

doi:10.1016/j.jfoodeng.2009.09.005

Cited by 103 publications

(55 citation statements)

References 16 publications

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“…Most research on hyperspectral imaging has focused on the reflectance measurement, which is appropriate for evaluating surface or subsurface quality characteristics but not for internal defects in food products. Our group proposed a new concept of integrating reflectance and transmittance measurements in one hyperspectral imaging system for detecting both external quality attributes (e.g., color and size) and internal defects [28,[82][83][84][85][86][87][88]. The concept is based on the fact that the visible light in the region of 400-675 nm, which is relatively poor in penetrating biological tissues, is more suitable for assessing surface features of the product, while the red and NIR light in the region of 675-1000 nm has better penetration capabilities and can thus be used for internal quality assessment in transmittance mode.…”

Section: Integrated Reflectance and Transmittance Imagingmentioning

confidence: 99%

“…The reflectance in the visible region, modelled by PLS, gave better predictions for skin color, with r 2 up to 0.76 and 0.75 for skin chroma and hue, respectively. Ariana and Lu [83] further investigated PCA for processing hyperspectral images for detection of internal defects in desalted whole pickles. The second principal component (PC) score images exhibited the strongest contrast between normal and defective pickles, which achieved an overall classification accuracy of 86% by a simple global thresholding method.…”

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

“…A branch and bound algorithm combined with the k-nearest neighbor (K-NN) classifier was utilized for wavelength selection. Two fourwavelength sets of 745, 805, 965 and 985 nm, and 745, 765, 885 and 965 nm were obtained, both of Ariana and Lu [83] further investigated PCA for processing hyperspectral images for detection of internal defects in desalted whole pickles. The second principal component (PC) score images exhibited the strongest contrast between normal and defective pickles, which achieved an overall classification accuracy of 86% by a simple global thresholding method.…”

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

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Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

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Section: Integrated Reflectance and Transmittance Imagingmentioning

confidence: 99%

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

Section: Quality Evaluation For Pickling Cucumbers and Picklesmentioning

confidence: 99%

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Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Huang

2017

Applied Sciences

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“…In some case the application on-line was achieved. Ariana & Lu (2010), evaluated the internal defect and surface color of whole pickles, in a commercial pickle processing. They used a prototype of on-line hyperspectral imaging system, operating in the wavelength range of 400-1000 nm.…”

Section: Image Analysismentioning

confidence: 99%

Chemometrics in Food Technology

Guidetti¹,

Beghi²,

Giovenzana³

2012

Chemometrics in Practical Applications

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“…Hyperspectral imaging technology has been used widely in detection of food defects (Kong et al, 2004), assessment of food quality (Ariana and Renfu, 2010;Fontaine et al, 2002;Lu and Peng, 2006;Martens and Martens, 1986;Okamoto and Lee, 2009), classification of near-isogenic crop genotypes (Nansen et al, 2008), and in detection of surface contaminations (Lefcout and Kim, 2006;Mehl et al, 2004;Park et al, 2006;Vargas et al, 2005). Comprehensive reviews of the use of imaging technology in detection of traits in food products have been published (Gowen et al, 2007;Wang and Paliwal, 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Nansen

Zhang

Aryamanesh

et al. 2014

Journal of Food Engineering

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In this study, we acquired 72 (training data) and 30 (independent validation) high-spatial resolution (7 by 7 pixels per mm 2 ) hyperspectral imaging data [240 spectral bands from 392 to 889 nm (spectral resolution = 2.1 nm)] from samples of field peas (Pisum sativum) with and without pea weevil (Bruchus pisorum) infestation. The reflectance data were analyzed with linear discriminant analysis (LDA) or either reflectance values only or of a combination of reflectance values and variogram parameters (derived from variogram analysis) from a single spectral band (782 nm). All examined classification models were assessed based on sensitivity (ability to positively detect infestation), specificity (ability to positively detect noninfestation), and accurate classification of 30 samples of independent validation data. Highest classification performance was obtained with a combination of reflectance values in two spectral bands (641 nm and 868 nm) and variogram parameters derived from 782 nm. This classification model was associated with a sensitivity of 94.7% and a specificity of 100%. In addition, all 30 independent validation data were accurately classified (100%). For comparison, traditional linear discriminant analyses of 108,351 reflectance profiles from individual pixels in the 72 samples or average reflectance profiles from the 72 samples classified the validation data with 84.0% and 83.3% accuracy, respectively. We are unaware of any reflectance-based classification system, which -based on reflectance data acquired in only three channels (spectral bands) -can provide this level of sensitivity and specificity and classification of independent validation data of internal defects in food products. Accurate and reliable classification of food objects based reflectance values in only a few spectral bands would likely imply low computer processing requirements and rapid data analysis. Thus, we believe that the current classification method may be useful for quality control systems of a wide range of food products.

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Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging

Cited by 103 publications

References 16 publications

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Chemometrics in Food Technology

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

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