The influence of nitrogen and biotin interactions on the performance of Saccharomyces in alcoholic fermentations
Bibliographic record
Abstract
AIM: To study the impact of assimilable nitrogen, biotin and their interaction on growth, fermentation rate and volatile formation by Saccharomyces. METHODS AND RESULTS: Fermentations of synthetic grape juice media were conducted in a factorial design with yeast assimilable nitrogen (YAN) (60 or 250 mg l(-1)) and biotin (0, 1 or 10 microg l(-1)) as variables. All media contained 240 g l(-1) glucose + fructose (1 : 1) and were fermented using biotin-depleted Saccharomyces cerevisiae strains EC1118 or UCD 522. Both strains exhibited weak growth and sluggish fermentation rates without biotin. Increased nitrogen concentration resulted in higher maximum fermentation rates, while adjusting biotin from 1 to 10 microg l(-1) had no effect. Nitrogen x biotin interactions influenced fermentation time, production of higher alcohols and hydrogen sulfide (H(2)S). Maximum H(2)S production occurred in the medium containing 60 mg l(-1) YAN and 1 microg l(-1) biotin. CONCLUSIONS: Nitrogen x biotin interactions affect fermentation time and volatile production by Saccharomyces depending on strain. Biotin concentrations sufficient to complete fermentation may affect the organoleptic impact of wine. SIGNIFICANCE AND IMPACT OF THE STUDY: This study demonstrates the necessity to consider nutrient interactions when diagnosing problem fermentations.
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 | 0.001 |
| 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 | 0.001 |
| 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".