Acetaldehyde kinetics of enological yeast during alcoholic fermentation in grape must
Bibliographic record
Abstract
Abstract Acetaldehyde strongly binds to the wine preservative SO2 and, on average, causes 50–70 mg l−1 of bound SO2 in red and white wines, respectively. Therefore, a reduction of bound and total SO2 concentrations necessitates knowledge of the factors that affect final acetaldehyde concentrations in wines. This study provides a comprehensive analysis of the acetaldehyde production and degradation kinetics of 26 yeast strains of oenological relevance during alcoholic fermentation in must under controlled anaerobic conditions. Saccharomyces cerevisiae and non-Saccharomyces strains displayed similar metabolic kinetics where acetaldehyde reached an initial peak value at the beginning of fermentations followed by partial reutilization. Quantitatively, the range of values obtained for non-Saccharomyces strains greatly exceeded the variability among the S. cerevisiae strains tested. Non-Saccharomyces strains of the species C. vini, H. anomala, H. uvarum, and M. pulcherrima led to low acetaldehyde residues (<10 mg l−1), while C. stellata, Z. bailii, and, especially, a S. pombe strain led to large residues (24–48 mg l−1). Acetaldehyde residues in S. cerevisiae cultures were intermediate and less dispersed (14–34 mg l−1). Addition of SO2 to Chardonnay must triggered significant increases in acetaldehyde formation and residual acetaldehyde. On average, 0.33 mg of residual acetaldehyde remained per mg of SO2 added to must, corresponding to an increase of 0.47 mg of bound SO2 per mg of SO2 added. This research demonstrates that certain non-Saccharomyces strains display acetaldehyde kinetics that would be suitable to reduce residual acetaldehyde, and hence, bound-SO2 levels in grape wines. The acetaldehyde formation potential may be included as strain selection argument in view of reducing preservative SO2 concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".