Effect of Aeration on Yeast Community Structure and Volatile Composition in Uninoculated Chardonnay Wines
Bibliographic record
Abstract
Uninoculated wines are regarded as having improved mouthfeel and texture and more complex flavor profiles when compared to wines inoculated with commercial S. cerevisiae strains. Uninoculated fermentation involves a complex microbial succession of yeasts and bacteria during fermentation. Microbial population dynamics are affected by several factors that can ultimately determine if a particular species or strain contributes to wine aroma and flavor. In this work, we have studied the effect of aeration, a common winemaking practice, on the yeast microbiota during uninoculated Chardonnay wine fermentation. The timing of aeration and then aeration intensity were evaluated across two successive vintages. While the timing of aeration significantly impacted fermentation efficiency across oxygen treatments, different levels of aeration intensity only differed when compared to the non-aerated control ferments. Air addition increased the viable cell population size of yeast from the genera Hanseniaspora, Lachancea, Metschnikowia and Torulaspora in both vintages. While in 2019, a high relative abundance was found for Hanseniaspora species in aerated ferments, in 2020, T. delbrueckii was visibly more abundant than other species in response to aeration. Accompanying the observed differences in yeast community structure, the chemical profile of the finished wines was also different across the various aeration treatments. However, excessive aeration resulted in elevated concentrations of ethyl acetate and acetic acid, which would likely have a detrimental effect on wine quality. This work demonstrates the role of aeration in shaping yeast population dynamics and modulating a volatile profile in uninoculated wines, and highlights the need for careful air addition to avoid a negative sensory impact on wine flavor and aroma.
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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) | 0.000 | 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".