Examining How the Fermentation Medium Influences Thiol Expression and Its Perceived Aroma in Commercial Brewing Yeast Strains
Bibliographic record
Abstract
In Saccharomyces, the IRC7 gene encodes for a cysteine S-conjugate β-lyase enzyme which can release polyfunctional thiols from their cysteinylated precursor forms, thereby promoting thiol aroma in beer. This study examined the thiol production of 10 commercial yeast strains in two different media, a hopped yeast extract–peptone–dextrose (YPD) medium and a 100% barley malt wort to explore how differences in yeast strain and medium conditions influence the release of polyfunctional thiols. 3-Sulfanylhexan-1-ol was most affected by medium conditions, and its concentrations were highest in wort fermentations. The higher nitrogen content and pH of the YPD medium relative to the wort fermentations were notable differences, and significant correlations between these variables and the extent of free thiol production were observed. A strong association existed between polyfunctional thiol concentrations and the fermentation-derived, malt, and hop-derived compounds 2-phenylethanol, β-damascenone, and β-ionone. The sensory impressions of thiol character in beer were influenced by the presence of other aromatic compounds such as esters and terpene alcohols, and aroma attributes such as “tropical” were not the most suitable for describing beers brewed with yeasts that fully express homozygous IRC7 F . Sensory attributes “sweaty”, “vegetal”, and “overripe fruit” were more strongly associated with these strains.
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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) | 0.001 | 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".