Characterization of three different classes of non-fermented teas using untargeted metabolomics

Qunfeng Zhang, Si Wu, Yan Li, Meiya Liu, Kang Ni, Xiaoyun Yi, Yuanzhi Shi, Lifeng Ma, Lothar Willmitzer, Jianyun Ruan

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Non-fermented teas, which are widely consumed in China, Japan, Korea, and elsewhere, have refreshing flavors and valuable health benefits. Various types of non-fermented teas look and taste similar and have no obvious differences in appearance, making their classification challenging. To date, there are very few reports about characterization and discrimination of different types of non-fermented teas. To characterize non-fermented teas and build a standard model for their classification based on their chemical composition, we employed multi-platform-based metabolomics to analyze primary and secondary metabolites in three main categories of non-fermented teas (green, yellow, and white), using 96 samples collected from China. Five hundred and ninety unique tea metabolites were identified and quantified in these three types of teas. Moreover, a partial least squares discriminant analysis (PLS-DA) model was established based on metabolomics data, in order to classify non-fermented teas into these three classes. Furthermore, our results speculate that the health benefits (e.g., antioxidant content) of these three types of non-fermented tea differ primarily because of variation in their metabolic components (e.g., ascorbate, vitexin).

Original languageEnglish
Pages (from-to)697-704
Number of pages8
JournalFood Research International
Volume121
DOIs
StatePublished - 1 Jul 2019
Externally publishedYes

Keywords

  • Characterization
  • Classification
  • Metabolomics
  • Non-fermented tea

ASJC Scopus subject areas

  • Food Science

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