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Qualitative identification of Bombyx batryticatus and its counterfeits using hyperspectral imaging and deep learning.

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Title: Qualitative identification of Bombyx batryticatus and its counterfeits using hyperspectral imaging and deep learning.
Authors: Li, Boxin1 (AUTHOR), Zhu, Shiping1 (AUTHOR) zspswu@126.com, Zhou, Shengling1 (AUTHOR) swuzhousl@163.com, Huang, Hua1 (AUTHOR), Liu, Hanbin2 (AUTHOR), Jiang, Hao3 (AUTHOR)
Source: Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy. May2026, Vol. 352, pN.PAG-N.PAG. 1p.
Subjects: Hyperspectral imaging systems, Deep learning, Silkworms, Forgery, Convolutional neural networks, Quality control
Geographic Terms: China
Abstract: Bombyx batryticatus, an important medicinal animal material in the traditional medicinal systems of China, Japan, and Korea, is in high demand due to its unique medicinal value. However, the widespread circulation of counterfeit Bombyx batryticatus (borax-treated) in the market poses a significant threat to the quality and safety of traditional Chinese medicines. This study proposes an efficient and non-destructive method for the authentication of Bombyx batryticatus by integrating hyperspectral imaging with deep learning techniques. For image-based analysis, a multi-feature fusion dataset was constructed by combining true-color composite images with features extracted from each hyperspectral channel via Principal Component Analysis. An improved ResNeXt50-KS model was proposed, integrating a Spatial and Channel Synergistic Attention (SCSA) module and Kolmogorov-Arnold network (KAN) On the fused feature dataset, the ResNeXt50-KS model achieved an authenticity classification accuracy of 98.58%. To delve deeper into the spectral characteristics of the samples, spectral data were augmented using a generative adversarial network (GAN), and a one-dimensional convolutional neural network (1D-CNN) was then constructed to automatically extract spectral features. This approach enabled both authenticity identification and geographical origin classification of Bombyx batryticatus. The 1D-CNN achieved an accuracy of 100% in authenticity discrimination. When transferred to the task of origin identification, the model maintained a high classification accuracy of 95.59%. The results demonstrate that the integration of hyperspectral and deep learning methods enables rapid and non-destructive identification of Bombyx batryticatus, providing effective technical support for quality control and market supervision of Chinese medicinal materials. [Display omitted] • Spectral image data of Bombyx batryticatus and counterfeit products were collected using hyperspectral imaging. • An improved ResNeXt50-KS image classification model was proposed. • A 1D-CNN was constructed to directly process spectral curves, enabling fine feature extraction and sample identification. • The method offers a non-destructive identification solution for quality control of traditional Chinese medicinal materials. [ABSTRACT FROM AUTHOR]
Copyright of Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191708494
AccessLevel: 6
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  Data: Qualitative identification of Bombyx batryticatus and its counterfeits using hyperspectral imaging and deep learning.
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  Label: Abstract
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  Data: Bombyx batryticatus, an important medicinal animal material in the traditional medicinal systems of China, Japan, and Korea, is in high demand due to its unique medicinal value. However, the widespread circulation of counterfeit Bombyx batryticatus (borax-treated) in the market poses a significant threat to the quality and safety of traditional Chinese medicines. This study proposes an efficient and non-destructive method for the authentication of Bombyx batryticatus by integrating hyperspectral imaging with deep learning techniques. For image-based analysis, a multi-feature fusion dataset was constructed by combining true-color composite images with features extracted from each hyperspectral channel via Principal Component Analysis. An improved ResNeXt50-KS model was proposed, integrating a Spatial and Channel Synergistic Attention (SCSA) module and Kolmogorov-Arnold network (KAN) On the fused feature dataset, the ResNeXt50-KS model achieved an authenticity classification accuracy of 98.58%. To delve deeper into the spectral characteristics of the samples, spectral data were augmented using a generative adversarial network (GAN), and a one-dimensional convolutional neural network (1D-CNN) was then constructed to automatically extract spectral features. This approach enabled both authenticity identification and geographical origin classification of Bombyx batryticatus. The 1D-CNN achieved an accuracy of 100% in authenticity discrimination. When transferred to the task of origin identification, the model maintained a high classification accuracy of 95.59%. The results demonstrate that the integration of hyperspectral and deep learning methods enables rapid and non-destructive identification of Bombyx batryticatus, providing effective technical support for quality control and market supervision of Chinese medicinal materials. [Display omitted] • Spectral image data of Bombyx batryticatus and counterfeit products were collected using hyperspectral imaging. • An improved ResNeXt50-KS image classification model was proposed. • A 1D-CNN was constructed to directly process spectral curves, enabling fine feature extraction and sample identification. • The method offers a non-destructive identification solution for quality control of traditional Chinese medicinal materials. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.saa.2026.127526
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Hyperspectral imaging systems
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Silkworms
        Type: general
      – SubjectFull: Forgery
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Quality control
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Qualitative identification of Bombyx batryticatus and its counterfeits using hyperspectral imaging and deep learning.
        Type: main
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          Name:
            NameFull: Li, Boxin
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            NameFull: Zhu, Shiping
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            NameFull: Zhou, Shengling
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            NameFull: Huang, Hua
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            NameFull: Liu, Hanbin
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            NameFull: Jiang, Hao
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          Dates:
            – D: 05
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 13861425
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            – Type: volume
              Value: 352
          Titles:
            – TitleFull: Spectrochimica Acta Part A: Molecular & Biomolecular Spectroscopy
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