Modulation of Volatile Thiol Release during Fermentation of Red Musts by Wine Yeast
Bibliographic record
Abstract
During the alcoholic fermentation of grape sugars, wine yeast produces a range of secondary metabolites that play a critical role in the aroma profile of wines. One of the most impactful yeast-modified compound families, particularly in white wines, are the ‘fruity’ polyfunctional thiols, which include 3-mercaptohexan-1-ol (3-MH) and 4-mercapto-4-methylpentan-2-one (4-MMP). While the formation and stylistic contribution of these thiols have been extensively researched in white wines, little is known about the conditions leading to their formation in red wines. In this study, we explored the ability of yeast strains to modulate the release of these aroma compounds during the fermentation of two red musts. In laboratory-scale Pinot Noir fermentations, the formation of 3-MH strongly correlated with yeast β-lyase activity, particularly with the presence of certain genotypes of the flavour-releasing gene IRC7. Subsequent production of Grenache wine at the pilot scale, with detailed compositional and sensory analysis, was undertaken to confirm laboratory-scale observations. A commercial wine strain used for expressing ‘fruity’ thiols in Sauvignon Blanc was shown to produce wines that exhibited more intense red fruit aromas. These results reveal an opportunity for winemakers to shape red wine aroma and flavour by using yeasts that might typically be considered for white wine production.
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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 | 0.001 | 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 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".