Nitrogen Availability and Utilisation of Oligopeptides by Yeast in Industrial Scotch Grain Whisky Fermentation
Bibliographic record
Abstract
Data files and R script for analysis included in the article "Nitrogen Availability and Utilisation of Oligopeptides by Yeast in Industrial Scotch Grain Whisky Fermentation". Abstract: Scotch grain whisky is produced with a substantial proportion of unmalted grains, which can result in nitrogen deficiency for yeast in the fermentable grain mash. This study examined nitrogen source availability and utilisation by three commercial whisky strains (Saccharomyces cerevisiae) during Scotch grain whisky fermentation, focusing on oligopeptides. Peptide uptake kinetics in synthetic whisky mash with defined peptides showed that oligopeptides of up to nine amino acids were taken up by the strains, albeit with some variability between the strains. The study found that peptides with appropriate molecular weights could replace free amino acids without negatively affecting fermentation kinetics. Moreover, fermentation performance improved when additional nitrogen was provided via peptides rather than diammonium phosphate. Analysis of industrial grain mash indicated that despite low initial yeast assimilable nitrogen, residual proteolytic activity from malt increased nitrogen availability during fermentation. Approximately 30% of the nitrogen consumed by yeast during grain mash fermentation was derived from peptides. LC-HRMS peptide analysis revealed complex dynamics of peptide formation, degradation, and utilisation. This study highlights the importance of oligopeptides in ensuring optimal fermentation efficiency in Scotch grain mash and similar substrates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.020 |
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".