Influence of Non-Saccharomyces on Wine Chemistry: A Focus on Aroma-Related Compounds
Bibliographic record
Abstract
Wine fermentation processes are driven by complex microbial systems, which comprise eukaryotic and prokaryotic microorganisms that participate in several biochemical interactions with the must and wine chemicals and modulate the organoleptic properties of wine. Among these, yeasts play a fundamental role, since they carry out the alcoholic fermentation (AF), converting sugars to ethanol and CO2 together with a wide range of volatile organic compounds. The contribution of Saccharomyces cerevisiae, the reference organism associated with AF, has been extensively studied. However, in the last decade, selected non-Saccharomyces strains received considerable commercial and oenological interest due to their specific pro-technological aptitudes and the positive influence on sensory quality. This review aims to highlight the inter-specific variability within the heterogeneous class of non-Saccharomyces in terms of synthesis and release of volatile organic compounds during controlled AF in wine. In particular, we reported findings on the presence of model non-Saccharomyces organisms, including Torulaspora delbrueckii, Hanseniaspora spp,Lachancea thermotolerans, Metschnikowia pulcherrima, Pichia spp. and Candida zemplinina, in combination with S. cerevisiae. The evidence is discussed from both basic and applicative scientific perspective. In particular, the oenological significance in different kind of wines has been underlined.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".