Chinese Spirits Identification Model Based on Mid-infrared Spectrum

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.