GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea
Xiaoli Yin, Wenjing Fu, Ying Chen, Ran-Feng Zhou, Weiqing Sun, Baomiao Ding, Xi-Tian Peng, Hui‐Wen Gu
Abstract
In this work, headspace gas chromatography coupled to mass spectrometry (HS-GC-MS) combined with multivariate statistical analysis was applied to reveal volatile markers from different grades of Yichang big-leaf green tea (YBGT). A total of 94 volatile organic compounds (VOCs) were detected and identified, which can be categorized as alkanes, terpene, aromatics, ketone, ester, alcohol, heterocyclic compounds, aldehyde, olefin, acid, amine, and nitrogen compounds. The differences between low-grade and high-grade YBGT were demonstrated by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Based on orthogonal partial least squares discriminant analysis (OPLS-DA), 19 VOCs were screened as markers for the discrimination of first-grade and second-grade YBGT, and 25 VOCs were screened as markers to distinguish first-grade from third-grade YBGT. Among them, 16 VOCs are common, which can be used as characteristic markers to distinguish low-grade from high-grade YBGT. Overall, our findings indicated that there are significant differences in VOCs among different grades of YBGT, and HS-GC-MS in combination with chemometric multivariate statistical analysis can be extended as a reliable strategy for discriminating grades of other Chinese green teas.