The influence of nitrogen and biotin interactions on the performance of Saccharomyces in alcoholic fermentations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it