Bioflavoring by non-conventional yeasts in sequential beer fermentations
Bibliographic record
Abstract
Non-conventional yeast species have great capacity for producing diverse flavor profiles in production of alcoholic beverages, but their potential for beer brewing, in particular in consecutive fermentations with Saccharomyces cerevisiae, has only poorly been explored. We have screened 17 non-conventional yeast species for production of an appealing profile of flavor esters and phenolics in the first phase of alcoholic fermentation, followed by inoculation with S. cerevisiae to complete the fermentation. For measurement of phenolic compounds and their precursors we developed an improved and highly sensitive methodology. The results show that non-conventional yeast species possess promising potential for enhancement of desirable flavors in beer production. Notable examples are increasing isoamyl acetate (fruity, banana flavor) by application of P. kluyverii, augmenting ethyl phenolic compounds (spicy notes) with Brettanomyces species and enhancing 4-vinyl guaiacol (clove-like aroma) with T. delbrueckii. All Pichia strains also produced high levels of ethyl acetate (solvent-like flavor). This might be selectively counteracted by selection of an appropriate S. cerevisiae strain for the second fermentation phase, which lowers total ester profile. Hence, optimization of the process conditions and/or proper strain selection in sequentially inoculated fermentations are required to unlock the full potential for aroma improvement by the non-conventional yeast species.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".