Differential Contribution of Jasmine Floral Volatiles to the Aroma of Scented Green Tea
Bibliographic record
Abstract
Tea volatiles’ generation and retention over manufacturing processes are crucial for tea quality. In this study, floral volatile adsorption and retention in green tea scented with Jasminum sambac flowers were examined over the scenting process. Out of 34 enhanced volatiles in the scented tea, β-ionone, β-linalool, indole, and methyl anthranilate were the most potent odorants with 5.1–45.2-fold higher odor activity values than the corresponding controls in the nonscented tea. Scenting efficiencies for the floral volatiles retained in the scented tea (the percentage of volatile abundance over its corresponding amount in jasmine flowers) ranged from 0.22% for α-farnesene to 75.5% for β-myrcene. Moreover, due to additional rounds of heat treatment for scented green tea manufacturing, some volatiles such as carotenoid-derived geraniol and β-ionone and lipid-derived ( Z )-jasmone were heat-enhanced and others such as nonanal were heat-desorbed in the scented green tea. Our study revealed that dynamic volatile absorption and desorption collectively determined tea volatile retention and tea aroma. Our findings may have a great potential for practical improvement of tea aroma.
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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".