Wu Zeng1, Zhanxiong Huo1, *, Yuxuan Xie2, Yingxiang Jiang1, Kun Hu1
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1869-1883, 2020, DOI:10.32604/cmc.2020.010139
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%… More >
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