Chemometric authentication of Pu'er teas in terms of multielement stable isotope ratios analysis by EA-IRMS and ICP-MS.

In this work, the stable isotope ratios of carbon, nitrogen, hydrogen, oxygen, and mineral elements and their stoichiometric methods were examined as possible factors that could certify Chinese tea based on its production years. A total of 43 multi-element stable isotope ratios of Xiangzhujing Pu'er tea in five production years were determined through inductively coupled plasma mass spectrometry (ICP-MS) and elemental analyzer-isotope ratio mass spectrometry (EA-IRMS) methods. Two unsupervised learning techniques (principal component analysis and hierarchical clustering analysis) and three supervised learning techniques (partial least squares discriminant analysis [PLS-DA], back-propagation artificial neural network [BP-ANN], and linear discriminant analysis [LDA]) were used on the basis of 18 statistically significant multi-elemental stable isotope ratios to build authentication models for Pu'er tea. The clustering abilities of the two unsupervised learning methods were worse than those of the three supervised learning methods. The three supervised models correctly separated the corresponding production years of the samples. The authentication performance was obtained through BP-ANN and LDA, with 100% recognition and prediction abilities, which were better than those of PLS-DA. δD, δ13C, and 154Sm/152Sm were determined as the markers for the accurate authentication of Pu'er tea in different production years. The profiles of multi-element stable isotope ratios obtained via ICP-MS and EA-IRMS with chemometric methods could serve as potential and powerful factors for authenticating Chinese tea in different production years. This study contributed to the generalization of the use of multi-elemental stable isotope ratio fingerprinting as a promising tool for testing the authenticity of tea worldwide.