Classification of Liquor Using Near-Infrared Spectroscopy and Chemometrics

被引：8

作者：

Tan, Chao ^{[1
,4
]}

Chen, Hui ^{[1
,2
]}

Lin, Zan ^{[3
]}

Wu, Tong ^{[1
]}

Wang, Li ^{[1
]}

Zhang, Kaishi ^{[1
]}

机构：

[1] Yibin Univ, Key Lab Proc Anal & Control Sichuan Univ, Yibin, Sichuan, Peoples R China

[2] Yibin Univ, Yibin, Sichuan, Peoples R China

[3] Chongqing Med Univ, Affiliated Hosp 1, Chongqing, Peoples R China

[4] Yibin Univ, Computat Phys Key Lab Sichuan Prov, Yibin, Sichuan, Peoples R China

来源：

ANALYTICAL LETTERS | 2015年 / 48卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Near-infrared spectroscopy; Chinese liquor; Chemometrics; NIR; SUCCESSIVE PROJECTIONS ALGORITHM; PARTIAL LEAST-SQUARES; IDENTIFICATION; FEASIBILITY;

D O I：

10.1080/00032719.2014.938343

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

The authenticity of Chinese liquor concerns consumer health and economic issues. The traditional characterization methods are time-consuming and require experienced analysts. The use of near-infrared (NIR) spectroscopy and chemometrics to classify Chinese liquor samples was investigated using 128 liquors. The spectral region between 5340cm(-1) and 7400cm(-1) was found to be most informative. Principal component analysis was employed to characterize liquor and principal components were extracted as inputs of training classifiers. Several supervised pattern recognition methods including K-nearest neighbor, perceptron, and multiclass support vector machine were used as algorithms of constructing classifiers. The initial principal components and all spectral variables were used as the input of training models. In terms of the misclassification ratio, the support vector machine approach was the most accurate. The results indicated that near-infrared spectroscopy and chemometrics are an alternative to conventional methods for the characterization of liquor.

引用

页码：291 / 300

页数：10