The Effects of Wort Valine Concentration on the Total Diacetyl Profile and Levels Late in Batch Fermentations with Brewing Yeast <i>Saccharomyces Carlsbergensis</i>
Bibliographic record
Abstract
Total diacetyl concentration often is used by the brewing industry to determine the time when fermentation or maturation is complete. As a result, fermenter or maturation vessel productivity often is dictated by total diacetyl concentration. Lab-scale batch fermentations using brewing yeast were carried out on wort containing different concentrations of free amino nitrogen (FAN). The resulting valine concentrations of these worts varied from 83 to 211 mg/L without altering the amino acid distribution. As the initial wort FAN and valine concentrations decreased, the rate of valine uptake increased, coinciding with lower final valine concentrations. Experiments using wort with initial valine concentrations of 130–140 mg/L showed the highest diacetyl levels late in fermentation. At more extreme shortages of valine (83–115 mg/L), a diacetyl double peak also occurred; however, the second peak occurred early enough in the fermentation that the yeast were able to reduce diacetyl to acceptable levels. Valine concentrations of 130–140 mg/L were found to be just below the critical concentration necessary for single-peak diacetyl profiles. The implications of these findings are greatest for breweries fermenting worts with valine concentrations just above the critical value for their set of fermentation conditions.
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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) | 0.001 | 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 | 0.000 |
| 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".