Wine characterisation according to geographical origin using analysis of mineral elements and rainfall correlation of oxygen isotope values

Abstract

SummaryThe 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 δ18O 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, δ18O 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.