Discrimination of Chinese rice wines of different geographical origins by UV-vis spectroscopy and chemometrics

Abstract

Discrimination of Chinese rice wines from four geographical origins ('Zhejiang', 'Jiangsu', 'Shanghai' and "Fujian") in China was investigated according to their UV-vis spectra. The UV-vis absorption spectra of 112 samples in the wavelength range of 190-800 nm were collected. Then the absorption data was subjected to principal component analysis to reveal differences between samples and the potential possibility of using multivariate methods to distinguish differences. Classification models were developed by soft independent modelling of class analogy, linear discriminate analysis (LDA), discriminant partial least squares and support vector machine. Seven mathematical pre-treatments were applied to improve the performance of the multivariate classification models. Among the seven pre-treatments, standard normal transformation (SNV) was superior to the other six methods. The results showed that, compared with other models, SNV-treated LDA models achieved better performances with an average correct classification rate of 98.96% in the training set and 100% in the testing set. The results demonstrate that UV spectroscopy combined with chemometric data analysis, as a rapid method to classify Chinese rice wines according to their geographical origins, is feasible.