Fermentative activity and production of volatile compounds by Saccharomyces grown in synthetic grape juice media deficient in assimilable nitrogen and/or pantothenic acid
Bibliographic record
Abstract
AIMS: To understand the impact of assimilable nitrogen and pantothenic acid on fermentation rate and synthesis of volatile compounds by Saccharomyces under fermentative conditions. METHODS AND RESULTS: A 2 x 3 factorial experimental design was employed with the concentrations of yeast assimilable nitrogen (YAN) (60 and 250 mg l(-1)) and pantothenic acid (10, 50 and 250 microg l(-1)) as variables. In media containing 250 microg l(-1) pantothenic acid, H2S production by two different species of Saccharomyces decreased when YAN was increased from 60 to 250 mg l(-1). Conversely, H2S production was significantly higher when the concentration of assimilable nitrogen was increased if pantothenic acid was deficient (10 or 50 microg l(-1)). Yeast synthesis of other volatile compounds were impacted by both assimilable nitrogen and pantothenic acid. CONCLUSIONS: While growth and fermentative rate of Saccharomyces was more influenced by nitrogen than by pantothenic acid, complicated interactions exist between these nutrients that affect the synthesis of volatile compounds including H2S. SIGNIFICANCE AND IMPACT OF THE STUDY: This study has important implications for the winemaking industry where a better understanding of the nutritional requirements of Saccharomyces is necessary to reduce fermentation problems and to improve final product quality.
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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 | 0.000 | 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".