Wine characterisation according to geographical origin using analysis of mineral elements and rainfall correlation of oxygen isotope values
Yingyue Su, Yan Zhao, Kexu Cui, Fei Wang, Jinjie Zhang, Ang Zhang
Abstract
Summary The wine industry has developed rapidly; however, wine fraud is a potential risk for consumers. In China, methods for detecting wine authenticity are far from perfect. To reduce the risk of counterfeit wines, Inductively Coupled Plasma‐Mass Spectrometry and Isotope Ratio Mass Spectrometry were used to geographically classify 104 wines from four major production areas. In this paper, the naturally distributed characteristics of thirty‐eight mineral elements contents and the effect of rainfall on the oxygen isotope values in wine were investigated. The result of δ 18 O ranged from −13‰ to 7‰. The oxygen isotope of wine water in Northwest China is obviously more positive than that in South China. Linear discriminant analysis (LDA) showed 88.5% classification accuracy in training set and 81.7% in the cross‐validation result. An artificial neural network (ANN) model determined origin of the wine with higher accuracy than LDA model. Furthermore, δ 18 O and Sr/Rb are important recognition elements in ANN, and the accuracy of region recognition can reach 90.9%. Thus, mineral elements and isotope ratios are effective in contributing to wine authenticity control in wine origin.