INTERACTIVE EFFECTS OF SELECTED NUTRIENTS AND FERMENTATION TEMPERATURE ON H<sub>2</sub>S PRODUCTION BY WINE STRAINS OF <i>SACCHAROMYCES</i>
Bibliographic record
Abstract
ABSTRACT Metabolic interactions between yeast assimilable nitrogen (YAN), biotin, pantothenic acid, and fermentation temperature that affect H2S production by wine yeast during alcoholic fermentation were examined. Strains of Saccharomyces cerevisiae (UCD 522 and EC1118) were inoculated into a synthetic grape juice medium with H2S evolution monitored under fermentative conditions. While a number of interactions affected the evolution of H2S, YAN as a factor by itself was found to be not significant (P > 0.05) for both yeasts examined. Maximal cumulative H2S production for strain UCD 522 occurred in media fermented at 30C with 60 mg/L YAN, 10 µg/L biotin, and 50 µg/L pantothenic acid while minimum production was observed with 250 mg/L YAN and 250 µg/L pantothenate. Similarly, strain EC1118 produced the most H2S at 30C, but with 250 mg/L YAN, 0.5 µg/L biotin, and 50 µg/L pantothenic acid and the least in media that contained 250 mg/L YAN and 250 µg/L pantothenic acid. PRACTICAL APPLICATIONS “Reduced” off‐odors of wines, primarily associated with sulfur‐containing molecules such as H2S, continue to be a difficulty facing winemakers worldwide. One strategy for wineries to limit these problems is to add yeast nutrients prior to fermentation, most commonly, nitrogen‐containing compounds such as diammonium phosphate. However, nitrogen deficiency is not always the sole cause for these problems. Rather, the current research suggests the need to consider factors other than nitrogen including availability of biotin and pantothenic acid as well as fermentation temperature in order to minimize these off‐odors.
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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 | 0.000 |
| 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".