As one of the most widely consumed alcoholic beverages, Chinese liquor varies greatly in price, flavor, and quality. This diversity calls for effective and reliable discrimination methods. In an attempt to find the best liquor discrimination method, this study used different methods to analyze and identify 730 Chinese liquor samples including 22 kinds, ten brands, and six flavors. These samples, covering most of the famous liquors in China, were analyzed by visible and near-infrared (Vis/NIR) spectroscopy and modeled by three classification methods including supporting vector machine, soft independent modeling of class analogy, and linear discriminate analysis based on principal component analysis (PCA-LDA). Pretreatments and parameters for each model were optimized, and models discrimination ability was compared. The research finds that PCA-LDA was the best model with an average prediction rate of 98.94 % in the training set and 95.70 % in the test set. The correct rates for brands, flavor styles, ages, and alcohol degrees were all higher than 95 %. It shows that Vis/NIR is a reliable, inexpensive, and effective tool for Chinese liquors discrimination.
Vis/NIR spectroscopy Chinese liquor Discrimination Chemometrics Fingerprint
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This work was kindly supported by the Fundamental Research Funds for the Central Universities (Program No. 2011PY010), the Scientific and Technological Project of Wuhan City of China (Program No.2013020501010171), and the National Natural Science Foundation of China (Program No. 30901007).
Compliance with Ethics Requirements
Zhao Li has no conflict of interest. Pei-Pei Wang has no conflict of interest. Chenchen Huang has no conflict of interest. Huan Shang has no conflict of interest. Si-Yi Pan has no conflict of interest. Xiu-Juan Li has no conflict of interest. This article does not contain any studies with human or animal subjects.
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