The Interaction of Two Saccharomyces cerevisiae Strains Affects Fermentation-Derived Compounds in Wine
Bibliographic record
Abstract
Previous winery-based studies showed the strains Lalvin® RC212 (RC212) and Lalvin® ICV-D254 (D254), when present together during fermentation, contributed to >80% relative abundance of the Saccharomyces cerevisiae population in inoculated and spontaneous fermentations. In these studies, D254 appeared to out-compete RC212, even when RC212 was used as the inoculant. In the present study, under controlled conditions, we tested the hypotheses that D254 would out-compete RC212 during fermentation and have a greater impact on key fermentation-derived chemicals. The experiment consisted of four fermentation treatments, each conducted in triplicate: a pure culture control of RC212; a pure culture control of D254; a 1:1 co-inoculation ratio of RC212:D254; and a 4:1 co-inoculation ratio of RC212:D254. Strain abundance was monitored at four stages. Inoculation ratios remained the same throughout fermentation, indicating an absence of competitive exclusion by either strain. The chemical profile of the 1:1 treatment closely resembled pure D254 fermentations, suggesting D254, under laboratory conditions, had a greater influence on the selected sensory compounds than did RC212. Nevertheless, the chemical profile of the 4:1 treatment, in which RC212 dominated, resembled that of pure RC212 fermentations. Our results support the idea that co-inoculation of strains creates a new chemical profile not seen in the pure cultures. These findings may have implications for winemakers looking to control wine aroma and flavor profiles through strain selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".