Modulation of the ‘flinty’ aroma compound phenylmethanethiol during fermentation: impacts of yeast starter culture and nitrogen supplementation
Bibliographic record
Abstract
The sulfur compound phenylmethanethiol (PMT) has been associated with ’flinty’ and ’struck-match’ aromas in wine, and it is considered to be an important contributor to the flavour of certain white wine styles. Little is known about the factors driving the formation of this potent sulfur aroma compound during winemaking conditions, as well as the precursors/pathways that might be involved in its formation. In this study, we demonstrate that a range of practical winemaking strategies may have significant implications on the final concentration of PMT in wine. Specifically, the choice of yeast strain used to perform alcoholic fermentation was shown to modulate the formation of this ‘flinty’ compound under laboratory-scale conditions, and in the absence of oak. The nutritional level of the fermentation media was also found to significantly impact PMT formation by yeast, as nitrogen additions in the form of inorganic nitrogen promoted the formation of PMT. Finally, the potential role of both benzaldehyde and hydrogen sulfide as precursors to PMT formation was also explored, as well as the contribution of other alternative pathways.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".