Profiling of dynamic changes in non-volatile metabolites of shaken black tea during the manufacturing process using targeted and non-targeted metabolomics analysis

文献类型： 外文期刊

第一作者： Xue, Jinjin

作者： Xue, Jinjin;Wang, Weiwei;Zhang, Jianyong;Wang, Wei;Le, Ting;Yin, Junfeng;Jiang, Heyuan;Xue, Jinjin;Ni, Dejiang;Liu, Panpan;Guo, Guiyi

作者机构：

关键词： Shaken black tea; Non-targeted metabolomics; Dipeptides; Amino acids; Metabolic pathways

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.056； 五年影响因子：6.295 ）

ISSN： 0023-6438

年卷期： 2022 年 156 卷

页码：

收录情况： SCI

摘要： Shaken black tea (SBT) is a new type of black tea that combines the traditional black tea processing technology with the shaking process of oolong tea. In this study, non-targeted metabolomics analysis based on ultra-highpressure liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) was applied to comprehensively analyze the non-volatile metabolites of SBT during the manufacturing process. Using multivariate statistical analysis, 93 compounds were screened as differential compounds. During the whole process of SBT, amino acids, dipeptides, theaflavins and lipids contents showed significant increases, while that of catechins and procyanidin C1 significantly decreased. Compared with fresh leaves, the content of proteinaceous amino acids (PAAs) in the dried black tea increased 1.61-fold, and total catechins decreased to 7.25%. The results of hierarchical clustering analysis (HCA) showed that fermentation is the key process that causes the variations of these metabolites, followed by drying and shaking. The biosynthesis metabolism of amino acids, flavonoids and linoleic acid are abundant, mainly due to the stress response of fresh tea leaves to dehydration and mechanical damage during shaking processing. It is beneficial to explore the quality formation during the production of SBT through comprehensive analysis of the changes in metabolites.

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[1]Profiling of dynamic changes in non-volatile metabolites of shaken black tea during the manufacturing process using targeted and non-targeted metabolomics analysis. Xue, Jinjin,Wang, Weiwei,Zhang, Jianyong,Wang, Wei,Le, Ting,Yin, Junfeng,Jiang, Heyuan,Xue, Jinjin,Ni, Dejiang,Liu, Panpan,Guo, Guiyi. 2022

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