Serial Re-Pitching of Yeast Impacts Final Flavor Profiles of Commercial Beer
Bibliographic record
Abstract
The aroma-active compounds produced by Saccharomyces cerevisiae during the fermentation of wort are key to the unique aroma and flavour profiles of beer. In commercial fermentations, there is batch-to-batch variation depending on yeast “brewing fitness” or the health of the yeast, but how does yeast health impact fermentation performance and metabolite production during fermentation? To address this, daily samples were collected from three full-scale commercial fermentations. The specific gravity was measured immediately, and samples were collected for carbohydrate analysis by High-Performance Liquid Chromatography and volatile compound analysis by Head-Space Gas Chromatography Mass Spectrometry (HS-GC-MS). Acetate esters (3), medium-chain fatty acid ethyl esters (7), hop-derived compounds (3), and an off-flavour (1) were detected and identified, and their relative signal was recorded for each sample. While there did not appear to be an effect of generational age on the duration of fermentation, age, in terms of the number of generations from serial re-pitching, impacted the ratios of volatile compounds. This difference in ratios was observed as early as Day 2, resulting in a difference in the volatile compound profiles of finished beers, therefore resulting in inconsistency in the product. This is important knowledge for brewers as generational age must be considered when fermenting high-quality, consistent products and monitoring fermentation progress/duration may not be enough to determine the ability of yeast to produce balanced flavour profiles.
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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 | 0.001 |
| 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 | 0.000 |
| 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".