4.5 Review

Metabolomics in quality formation and characterisation of tea products: a review

期刊

出版社

WILEY
DOI: 10.1111/ijfs.15767

关键词

metabolomics; tea identification; tea processing; tea quality

资金

  1. Research Initiation Funding of Zhejiang Sci-Tech University [19042112-Y]
  2. National Support Funding for Reform and Development of Local Universities [303013-2021-0007]

向作者/读者索取更多资源

This article summarizes the application of metabolomics in tea, including the main metabolites in tea and their implications on tea quality, research on optimizing tea processing steps, and methods to distinguish different grades of tea through metabolomics. These studies provide guidance for improving tea quality and highlight potential challenges.
Tea is one of the biggest categories of modern non-alcoholic beverages with health benefits. Through different processes, tea products are divided into six main types: green tea, yellow tea, white tea, oolong tea, black tea and dark tea. Metabolomics is helpful for the comprehensive, accurate and rapid determination of tea metabolites in different types of tea products and tea processing materials. It has been widely applied in studying the relationship of tea metabolites and its quality. Therefore, it is necessary to summarise the application of metabolomics in tea. In this review, we described and summarised (1) the main metabolites of six types of tea and their implications on tea quality with emphasis on the content differences in the main metabolites in different types of tea; (2) the research on optimising the key processing steps of six types of tea based on metabolomics and the discussion of various new strategies for effectively improving their quality and (3) the advanced approach to characterise and distinguish different grades of tea products via metabolomics. The current review offers guidance for the improvement of tea quality by metabolomics and its potential challenges.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据