Changes in the Concentration of Yeast-Derived Volatile Compounds of Red Wine during Malolactic Fermentation with Four Commercial Starter Cultures of<i>Oenococcus oeni</i>
Bibliographic record
Abstract
The effects of malolactic fermentation (MLF) on the concentration of volatile compounds released by yeasts during the production of red wine were investigated by inoculation with four commercial starters of Oenococcus oeni. Volatile compounds in wine at the end of MLF were extracted, analyzed by GC-MS and GC, and compared with those extracted form a noninoculated reference sample. Several esters known to play a role in the aroma profile of red wine, such as C4-C8 ethyl fatty acid esters and 3-methylbutyl acetate, were found to increase with MLF, and their final concentration was dependent on the bacterial starter employed for the induction of MLF. The overall increase of ethyl fatty acid esters was generally larger than the one observed for acetate esters. Ethyl lactate, 3-hydroxybutanoate, 2-phenylethanol, methionol, and gamma-butyrolactone were also increased by bacterial metabolism. The impact of MLF on other volatiles or red wine, including several higher alcohols, fatty acids, and nitrogen compounds, was generally negligible.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".