Multivariate Analysis Based on GC-MS Fingerprint and Volatile Composition for the Quality Evaluation of Pu-Erh Green Tea

Abstract

Based on the fully automatic headspace solid-phase microextraction (HS-SPME)/gas chromatography-mass spectrometry (GC-MS) and multivariate statistical methods, a novel model of identifying and evaluating the quality of Yunnan Pu-erh green tea was constructed for the first time in this work. Twelve Pu-erh green teas from 12 typical production sites of Pu-erh district in Yunnan Province and 6 regular green teas from Zhejiang, Sichuan, Anhui, Henan, Hubei, and Jiangsu provinces of China were used to construct the model. Data from 18 green tea samples by GC-MS were processed with fingerprint technology and chemometric methods. The GC-MS fingerprints from 12 Pu-erh green teas whose correlation coefficients and congruence coefficients were over 0.850 and demonstrated Pu-erh green tea samples from different production sites in Yunnan were consistent to some extent in spite of slightly different chemical indexes. A total of 77 volatile compounds were identified in 18 green teas, mainly including linalool, linalool oxides, phytol, caffeine, geraniol, and dihydroactinidiolide, and their chemical compositions were slightly similar. Cluster analysis (CA) and principal component analysis (PCA) demonstrated that 12 Pu-erh green teas could be clearly distinguished from other six regular green teas according to their chemical characteristics. Our results thus indicate that the chromatographic fingerprint combined with multivariate statistical techniques is useful for the identity and consistency evaluation of Pu-erh green teas. Such an approach is believed to be equally applicable to other green teas.