Flavor changes of Yunnan large-leaf cultivar white tea during different aging periods
Bibliographic record
Abstract
Yunnan large-leaf white tea gains popularity for its unique flavor attributable to the local tea varieties and environments. But how aging crucially enhances its quality and flavor remains unclear. Here, flavor change and safety quality of “Qinghuan” (QH) stored for different periods were studied, revealing that older teas developed sweeter, smoother, and more balanced flavors, with a stable and pleasant aroma. In total, 32 key non-volatiles (e.g., rutin, (−)-epigallocatechin gallate, p-coumaric acid) were screened by principal component analysis and significant changes were observed in volatiles (alcohols, aldehydes, terpenes) over time. Additionally, aflatoxins in QH teas remained undetected, irrespective of aging time; and oral median lethal doses of 2023 and 2017 QH teas were 17.17 g/kg·bw and 15.09 g/kg·bw, indicating that QH tea has a high safety quality for drinking over the aging time. Overall, QH tea's flavor and safety quality increased with storage time, providing evidence of the temporal value for collecting white tea. • Storage duration significantly impacted the color, aroma, and taste of white tea . • α -ionone, β -cyclocitral, tetradecane, and dehydro- β -ionone were the key VOCs in QH. • The one-year and seven-year aged white teas were classified as practically non-toxic. • Caffeine and ellagic acid affected liquor color and flavor of QH during storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".