Unveiling metabolite network dynamics during Pu-erh tea storage via non-targeted metabolomics

Pu-erh tea's quality and price are inextricably intertwined with age, but false claims about production year can harm both consumers and producers. While tea quality is currently assessed subjectively by experts, developing a scientific method to determine storage time is crucial for the tea industry's growth and integrity. In the present study, analysis of Pu-erh tea (PT) with different storage times showed that the storage process enhanced the complexity of the metabolite networks of extracts and flocs, but the metabolic network characteristics of tea flocs did not exhibit significant changes with the progression of storage time. It is noteworthy that the 41 storage-related core metabolites are important to the storage process; the storage score based on these metabolites was related to the biochemical indicators of the extract and the storage time. Our findings were validated in another independent metabolic data set of green tea (GT), oolong tea (OT), Assam black tea (ABT) and PT. Finally, our regression model based on the storage score could accurately predict the storage time of different teas (R 2 = 95.70%). Overall, the findings of our have important guiding significance for expanding the methods for predicting tea storage duration. • The Pu-erh tea storage process enhanced the complexity of the metabolite network. • Metabolite network analysis of Pu-erh tea identified 41 storage-related core metabolites. • Storage score model was established based on 41 storage-related core metabolites. • The storage score model could predict the storage time of tea.

Authors

Publication Details

Journal
LWT
Published
2024-09-17
DOI
https://doi.org/10.1016/j.lwt.2024.116789
Citations
7
Primary Topic
Tea Polyphenols and Effects
Type
article
Field-Weighted Citation Impact
2.97
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article

Unveiling metabolite network dynamics during Pu-erh tea storage via non-targeted metabolomics

Shan Xiao, Jihui Wang, Da Ma, Ruidong Yang et al.
7 citations
LWT
Tea Polyphenols and Effects
2.97
article

Unveiling metabolite network dynamics during Pu-erh tea storage via non-targeted metabolomics

Shan Xiao, Jihui Wang, Da Ma, Ruidong Yang, Yu Pang, Jiawei Luo, Ruihong Xie, Bo Wang
article en
7 citations

Abstract

Pu-erh tea's quality and price are inextricably intertwined with age, but false claims about production year can harm both consumers and producers. While tea quality is currently assessed subjectively by experts, developing a scientific method to determine storage time is crucial for the tea industry's growth and integrity. In the present study, analysis of Pu-erh tea (PT) with different storage times showed that the storage process enhanced the complexity of the metabolite networks of extracts and flocs, but the metabolic network characteristics of tea flocs did not exhibit significant changes with the progression of storage time. It is noteworthy that the 41 storage-related core metabolites are important to the storage process; the storage score based on these metabolites was related to the biochemical indicators of the extract and the storage time. Our findings were validated in another independent metabolic data set of green tea (GT), oolong tea (OT), Assam black tea (ABT) and PT. Finally, our regression model based on the storage score could accurately predict the storage time of different teas (R 2 = 95.70%). Overall, the findings of our have important guiding significance for expanding the methods for predicting tea storage duration. • The Pu-erh tea storage process enhanced the complexity of the metabolite network. • Metabolite network analysis of Pu-erh tea identified 41 storage-related core metabolites. • Storage score model was established based on 41 storage-related core metabolites. • The storage score model could predict the storage time of tea.

LWTVol. 209
Openalex Percentile: Top 9%
Tea Polyphenols and Effects
2.97
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Unveiling metabolite network dynamics during Pu-erh tea storage via non-targeted metabolomics — Shan Xiao, Jihui Wang, et al. · LWT (2024) | TGRS Research Map | TGRS