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Chinese Spirits Identification Model Based on Mid-Infrared Spectrum

Wu Zeng1, Zhanxiong Huo1, *, Yuxuan Xie2, Yingxiang Jiang1, Kun Hu1
1 School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, 430000, China.
2 Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, H3G 1M8, Canada.
* Corresponding Author: Zhanxiong Huo. Email: .

Computers, Materials & Continua 2020, 64(3), 1869-1883.

Received 13 February 2020; Accepted 03 May 2020; Issue published 30 June 2020


Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features but only achieved an 83% classification accuracy. Although both approaches were better than a direct SVM approach based on the classification results (64% classification accuracy), they also demonstrated the importance of extracting sparse PCs, which captured most important information. The combination of computer technology SPCA and MIR provides a new and convenient method for liquor identification in food safety.


Mid-infrared spectra, Chinese spirits, SPCA, SVM, liquor identification.

Cite This Article

W. Zeng, Z. Huo, Y. Xie, Y. Jiang and K. Hu, "Chinese spirits identification model based on mid-infrared spectrum," Computers, Materials & Continua, vol. 64, no.3, pp. 1869–1883, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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