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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: email.

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


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.


Cite This Article

APA Style
Zeng, W., Huo, Z., Xie, Y., Jiang, Y., Hu, K. (2020). Chinese spirits identification model based on mid-infrared spectrum. Computers, Materials & Continua, 64(3), 1869-1883.
Vancouver Style
Zeng W, Huo Z, Xie Y, Jiang Y, Hu K. Chinese spirits identification model based on mid-infrared spectrum. Comput Mater Contin. 2020;64(3):1869-1883
IEEE Style
W. Zeng, Z. Huo, Y. Xie, Y. Jiang, and K. Hu "Chinese Spirits Identification Model Based on Mid-Infrared Spectrum," Comput. Mater. Contin., vol. 64, no. 3, pp. 1869-1883. 2020.

cc 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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