Impact of Artificially Induced Respiratory Deficient Yeast on Beer Flavor and Fermentation
Bibliographic record
Abstract
Respiratory deficient cells or “petites” are the most common type of mutation found in brewing yeast. High levels of petites are known to contribute to unwanted flavors in beer along with yeast flocculation problems during fermentation. However, a minimal amount is known regarding the impact of petites when present at naturally occurring frequencies. Accordingly, this study investigated if petites, which are present at low frequencies, affect beer flavor and fermentation profiles. Laboratory [20 mL] fermentations were undertaken with yeast that contained a range of petite populations 3.7, 5.1, 8.7, and 10.8%. During fermentation, the yeast in suspension, wort density, and alcohol were monitored. At the end of the fermentation, the beer was analyzed for volatile flavor compounds. Correlations between petite levels and levels of vicinal diketones, acetate esters, and medium chain fatty acid (MCFA) ethyl esters existed. Higher alcohol levels were unchanged (propan-1-ol, 3-methyl butanol, 2-methyl butanol, and isobutanol) with increasing levels of petite concentrations. Similarly, the yeast in suspension behavior and the change in wort density attenuation between the control and petite enriched fermentations were not significantly different (P > 0.05). This study suggests that low concentrations of petites in the pitched yeast would not be detectable in the final product characteristics.
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 | 0.000 |
| 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 | 0.001 | 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".