Quantitative analysis of starch species based on near-infrared spectroscopy and quaternion convolution neural network

Ailing Tan; Jing Zhao; Yunxin Wang; Xiangpeng Wu; Xiaohang Li; Pengfei Pei; Yajie Zuo; Yong Zhao

doi:10.1117/12.2651759

20 January 2023 Quantitative analysis of starch species based on near-infrared spectroscopy and quaternion convolution neural network

Ailing Tan, Jing Zhao, Yunxin Wang, Xiangpeng Wu, Xiaohang Li, Pengfei Pei, Yajie Zuo, Yong Zhao

Author Affiliations +

Proceedings Volume 12558, AOPC 2022: Optical Spectroscopy and Imaging; 125580H (2023) https://doi.org/10.1117/12.2651759
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

In this paper, the near-infrared spectral data of five different types of starch were collected, and the starch species identification model was constructed by using a quaternion convolutional neural network (QCNN), we proved that the qualitative model based on QCNN has obtained higher prediction accuracy than traditional qualitative models. In the experimental results, the classification accuracy of QCNN for five different starches reached 0.996. The results show that the combination of the quaternion spectral fusion method and deep learning is more conducive to extracting and mining the deep information of NIR spectra and has important research significance and application value in the field of near-infrared spectroscopy technology

Citation Download Citation

Ailing Tan, Jing Zhao, Yunxin Wang, Xiangpeng Wu, Xiaohang Li, Pengfei Pei, Yajie Zuo, and Yong Zhao "Quantitative analysis of starch species based on near-infrared spectroscopy and quaternion convolution neural network", Proc. SPIE 12558, AOPC 2022: Optical Spectroscopy and Imaging, 125580H (20 January 2023); https://doi.org/10.1117/12.2651759

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