GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea. (15th December 2022)

• Record Type: Journal Article
• Authors: Yin, Xiao-Li; Fu, Wen-Jing; Chen, Ying; Zhou, Ran-Feng; Sun, Weiqing; Ding, Baomiao; Peng, Xi-Tian; Gu, Hui-Wen
• Is Part Of: Lebensmittel-Wissenschaft + Technologie =. Volume 171(2022)
• Publication Date: 2022-12-15
• Subjects: Headspace gas chromatography coupled to mass spectrometry -- Volatile organic compounds -- Multivariate statistical analysis -- Chinese green tea -- Untargeted metabolomics; Food industry and trade -- Periodicals; Food -- Composition -- Periodicals; Microbiology -- Periodicals; Nutrition -- Periodicals; 664.005
• DOI: 10.1016/j.lwt.2022.114148
• Languages: English
• ISSNs: 0023-6438

Abstract

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. Highlights: Untargeted GC-MS was developed for revealing key VOCs in Yichang big-leaf green tea. A total of 94 volatile organic compounds were detected and identified by the proposed method. The differences between low-grade and high-grade teas were demonstrated by PCA and HCA. 16 common VOCs were screened and identified by OPLS-DA as characteristic markers. The proposed method can be a reliable strategy for discriminating grades of Chinese green teas.