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  • 1
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    Non-destructive detection and classification of in-shell insect-infested almonds based on multispectral imaging technology (2019)
    Cambridge University Press
    Details
    Publication Date: 2019
    Description: 〈div data-abstract-type="normal"〉〈p〉The feasibility of non-destructive detection and classification of in-shell insect-infested almonds was examined by using multispectral imaging (MSI) technology combined with chemometrics. Differentiation of reflectance spectral data between intact and insect-infested almonds was attempted by using analytical approaches based on principal component analysis and support vector machines, classification accuracy rates as high as 99.1% in the calibration set and 97.5% in the prediction set were achieved. Meanwhile, the in-shell almonds were categorized into three classes (intact, slightly infested and severely infested) based on the degree of damage caused by insect infestation and were characterized quantitatively by the analysis of shell/kernel weight ratio. A three-class model for the identification of intact, slightly infested and severely infested almonds yielded acceptable classification performance (95.6% accuracy in the calibration set and 93.3% in the prediction set). These results revealed that MSI technology combined with chemometrics may be a promising approach for the non-destructive detection of hidden insect damage in almonds and could be used for industrial applications.〈/p〉〈/div〉
    Print ISSN: 0021-8596
    Electronic ISSN: 1469-5146
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Cambridge University Press
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  • 2
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    Non-destructive detection and classification of in-shell insect-infested almonds based on multispectral imaging technology (2018)
    Cambridge University Press
    Details
    Publication Date: 2018-11-01
    Description: The feasibility of non-destructive detection and classification of in-shell insect-infested almonds was examined by using multispectral imaging (MSI) technology combined with chemometrics. Differentiation of reflectance spectral data between intact and insect-infested almonds was attempted by using analytical approaches based on principal component analysis and support vector machines, classification accuracy rates as high as 99.1% in the calibration set and 97.5% in the prediction set were achieved. Meanwhile, the in-shell almonds were categorized into three classes (intact, slightly infested and severely infested) based on the degree of damage caused by insect infestation and were characterized quantitatively by the analysis of shell/kernel weight ratio. A three-class model for the identification of intact, slightly infested and severely infested almonds yielded acceptable classification performance (95.6% accuracy in the calibration set and 93.3% in the prediction set). These results revealed that MSI technology combined with chemometrics may be a promising approach for the non-destructive detection of hidden insect damage in almonds and could be used for industrial applications.
    Print ISSN: 0021-8596
    Electronic ISSN: 1469-5146
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Cambridge University Press
    Location Call Number Expected Availability
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    PAPER CURRENT
  • 3
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    Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods (2014)
    Cambridge University Press
    Details
    Publication Date: 2014-11-10
    Description: SUMMARYSoybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.
    Print ISSN: 0021-8596
    Electronic ISSN: 1469-5146
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Published by Cambridge University Press
    Location Call Number Expected Availability
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    PAPER CURRENT
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