@Article{cmc.2020.010139, AUTHOR = {Wu Zeng, Zhanxiong Huo, *, Yuxuan Xie, Yingxiang Jiang, Kun Hu}, TITLE = {Chinese Spirits Identification Model Based on Mid-Infrared Spectrum}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {64}, YEAR = {2020}, NUMBER = {3}, PAGES = {1869--1883}, URL = {http://www.techscience.com/cmc/v64n3/39464}, ISSN = {1546-2226}, ABSTRACT = {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.}, DOI = {10.32604/cmc.2020.010139} }