Author(s)

Bin Yang¹, Lijun Yao^1,2, Tao Pan^1*

¹Department of Optoelectronic Engineering, Jinan University, Guangzhou, China.
²Guangzhou SonDon Network & Technology Co., Ltd., Guangzhou, China.

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

Identification of Liquor Brands, Near-Infrared Spectroscopy, Partial Least Squares Discriminant Analysis, Principal Component Linear Discriminant Analysis

Yang, B. , Yao, L. and Pan, T. (2017) Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands. Engineering, 9, 181-189. doi: 10.4236/eng.2017.92009.