Journal of Applied Spectroscopy

, Volume 85, Issue 5, pp 949–952 | Cite as

Accurate Identification of the Sex and Species of Silkworm Pupae Using Near Infrared Spectroscopy

  • Dan Tao
  • Zhengrong Wang
  • Guanglin LiEmail author
  • Guangying Qiu

The present study proposes a novel method to discriminate the sex and species of silkworm pupae using NIR spectroscopy (800–2778 nm). The spectra from 840 silkworm pupae were collected then divided into a calibration set (700) and a test set (140) using the Kennard–Stone (KS) algorithm. The recognition models were built using the radial basis function and neural network (RBF–NN) and support vector machine (SVM) approaches. The species and sex identification results using the RBF–NN and SVM models based on full spectral data achieved a low accuracy of 5% and 33.57%, respectively. To improve the accuracy and decrease the processing time, both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the data dimensions. The performance of the optimized SVM model (92.14%) was much better than the RBF–NN model (19.29%) based on PCA. Overall, the best discrimination results were obtained using the RBF–NN and SVM models based on LDA, providing an accuracy of 100%. These promising results have shown that the LDA–SVM and LDA–RBF–NN models can accurately recognize the sex and species of silkworm pupae using NIR spectroscopy.


silkworm pupa NIR spectroscopy sex species 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Dan Tao
    • 1
  • Zhengrong Wang
    • 1
  • Guanglin Li
    • 1
    Email author
  • Guangying Qiu
    • 1
  1. 1.College of Engineering and TechnologySouthwest UniversityChongqingChina

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