Nondestructive detection of internal insect infestation in jujubes using visible and near-infrared spectroscopy

Wang, J.; Nakano, K.; Ohashi, Shintaroh

doi:10.1016/j.postharvbio.2010.09.017

Cited by 42 publications

(22 citation statements)

References 19 publications

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“…In a recent study by Nansen et al, 41 reflection data were acquired from two crop-insect pest systems: Several studies examined the importance of spectral data reduction in the quality control of food products using hyperspectral imaging analysis. Ariana and Renfu 14 used five pixel resolutions (5,10,20,40, and 60 nm) to classify pickling cucumbers (Cucumis sativus) and whole pickles with and without internal defects based on partial least squares discriminant analysis. The authors found the highest classification accuracy at pixel resolutions of 20 and 40 nm (compared to pixel resolutions of 5, 10, and 60 nm).…”

Section: Discussionmentioning

confidence: 99%

“…5,6 The common denominator in these reflection-based applications is to accurately and consistently detect changes in reflection profiles and associate such changes with damage or loss in food quality. Because insect-induced damage often causes only subtle changes in the reflection profiles acquired from food products [1][2][3][4][5][6] and crops, 7,8 the use of sensitive, reliable, and robust classification methods is of paramount importance. A support vector machine (SVM) seeks a decision boundary, providing a trade-off between fitting the training data and hypothesis space complexity.…”

Section: Introductionmentioning

confidence: 99%

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Importance of Spatial and Spectral Data Reduction in the Detection of Internal Defects in Food Products

Zhang

Nansen²,

Aryamanesh

et al. 2015

Appl Spectrosc

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Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P , 0.01). There was a negative association between pixel resolution and classification accuracy, while spectral binning equivalent to up to 98% data reduction had negligible effect on classification accuracies. This study supports the potential use of reflection-based technologies in the quality control of food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Importance of Spatial and Spectral Data Reduction in the Detection of Internal Defects in Food Products

Zhang

Nansen²,

Aryamanesh

et al. 2015

Appl Spectrosc

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“…The approach has been used to detect damage and internal infestation in food products, including field peas (Pisum sativum) (100,158), wheat kernels (Triticum aestivum) (125,126), soy beans (Glycine max) (52), and jujubes (Ziziphus jujuba) (139,140). In addition, thermal imaging (reflectance in the 8-12 µm range) has been used to detect infestations by a stored grain beetle (Cryptolestes ferrugineus) inside wheat kernels (80) and infestations by insects in a wide range of other food products (136).…”

Section: Cryptic Insect Infestationsmentioning

confidence: 99%

Remote Sensing and Reflectance Profiling in Entomology

Nansen

Elliott

2016

Annu. Rev. Entomol.

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Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

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“…Such classifications include levels of purity of pharmaceutical samples (Amigo and Ravn, 2009;Gowen et al, 2008Gowen et al, , 2011Ravn et al, 2008), and food products (Huang et al, 2014;Lefcout and Kim, 2006;Park et al, 2006;Vargas et al, 2005). 2) To classify food objects with or without particular defects (Gaston et al, 2011;Heitschmidt et al, 2004;Nansen et al, 2014;Singh et al, 2009Singh et al, , 2010Wang et al, 2010Wang et al, , 2011Zhang et al, 2015) or food into specific classes Blasco et al, 2003;Cubero et al, 2011;Kamruzzaman et al, 2012). There are several important and comprehensive reviews of applications of hyperspectral imaging in studies of both food quality and food safety Feng and Sun, 2012;Huang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes

Nansen

Singh

Mian

et al. 2016

Journal of Food Engineering

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a b s t r a c tThe uniqueness and consistency of commercial food and beverage brands are critically important for their marketability. Thus, it is important to develop quality control tools and measures, so that both companies and consumers can monitor whether a given food product or beverage meets certain quality expectations and/or is consistent when purchased at different times or at different locations. In this study, we characterized the consistency (levels of extractable protein and reducing sugars) of 15 brands of roasted coffee beans, which were obtained from a supermarket at two dates about six months apart. Coffee brands varied markedly in extractable protein and reducing sugar contents between dates, and also within and among roasting classes (light, medium, medium-dark, and dark roasts). We acquired hyperspectral imaging data (selected bands out of 220 narrow spectral bands from 408 nm to 1008 nm) from ground samples of the roasted coffee beans, and reflectance-based classification of roasting classes was associated with fairly low accuracy. We provide evidence that the combination of hyperspectral imaging and a general quality indicator (such as extractable protein content) can be used to monitor brand consistency and quality control. We demonstrated that a non-destructive method, potentially realtime and automated, and quantitative method can be used to monitor the consistency of a highly complex beverage product. We believe the results from this study of brand consistency are not only of relevance to the coffee industry but to a wide range of commercial food and beverage brands.

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Nondestructive detection of internal insect infestation in jujubes using visible and near-infrared spectroscopy

Cited by 42 publications

References 19 publications

Importance of Spatial and Spectral Data Reduction in the Detection of Internal Defects in Food Products

Importance of Spatial and Spectral Data Reduction in the Detection of Internal Defects in Food Products

Remote Sensing and Reflectance Profiling in Entomology

Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes

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