Integrated untargeted and targeted metabolomics and microbiome profiling reveal the effects of storage duration on the flavor quality of Rizhao Jinhua white tea
Bibliographic record
Abstract
The post-fermented tea develops enhanced quality attributes with prolonged storage. In this study, we explored the dynamic changes in sensory characteristics, untargeted and targeted metabolomics, and microbial communities of Rizhao Jinhua white tea (WFB) across different storage years. Storage process reduced bitterness and astringency, while improving overall mellowness. Levels of total polyphenols, amino acids, theaflavins, and thearubigins declined significantly, whereas theabrownin reached its peak at year 5. Among 118 identified differential metabolites, flavonoids exhibited the most pronounced variations. Further targeted quantification of flavonoids revealed catechin, epicatechin, epigallocatechin, quercitrin, and isorhamnetin as key flavor determinants. This was related to glycosylation, hydrogenation, hydroxylation, and hydrolysis reactions occurring during storage. Dominant microbial genera such as Aspergillus and Pseudomonas continuously promoted flavonoids transformations during storage. The outcomes of this research support better approaches to flavor quality optimization in stored Jinhua white tea. • A Jinhua white tea product was developed based on the fermentation of white tea. • Jinhua white tea stored 5 years reduces bitterness-astringency, enhances mellowness. • Flavonoids are the key metabolites responsible for flavor changes. • Aspergillus and Pseudomonas continuously contribute to metabolite transformation. • First establishment of sensory-metabolite-microbe network in Jinhua white tea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".