@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}
}