Multi-element composition and isotopic signatures for the geographical origin discrimination of green tea in China: A case study of Xihu Longjing

Abstract Reliable discrimination of the geographical origin of tea, especially very well-known teas, is crucial for market developing and consumer rights’ protection. In this study, multi-element contents and stable isotope signatures in the flat-shaped green tea samples collected from different producing areas were assayed. Linear discrimination analysis (LDA), partial least squares discrimination analysis (PLS-DA), and a decision tree (DT) were tested for their ability to discriminate the tea’s geographical origin. Under the validation by cross-validation and “blind” dataset, the prediction accuracies of the three methods were all greater than 70%. The DT method showed the best performance, with an accuracy of 90%. Furthermore, for the discrimination of Xihu Longjing (XHLJ) green tea, DT also showed the lowest error rate of 1.5% (1 of 67 wrongly classified to XHLJ), which was better than the 6% rate observed for PLS-DA.