Potential Grape-Derived Contributions to Volatile Ester Concentrations in Wine
Bibliographic record
Abstract
Grape composition affects wine flavour and aroma not only through varietal compounds, but also by influencing the production of volatile compounds by yeast. C9 and C12 compounds that potentially influence ethyl ester synthesis during fermentation were studied using a model grape juice medium. It was shown that the addition of free fatty acids, their methyl esters or acyl-carnitine and acyl-amino acid conjugates can increase ethyl ester production in fermentations. The stimulation of ethyl ester production above that of the control was apparent when lower concentrations of the C9 compounds were added to the model musts compared to the C12 compounds. Four amino acids, which are involved in CoA biosynthesis, were also added to model grape juice medium in the absence of pantothenate to test their ability to influence ethyl and acetate ester production. β-Alanine was the only one shown to increase the production of ethyl esters, free fatty acids and acetate esters. The addition of 1 mg∙L(-1) β-alanine was enough to stimulate production of these compounds and addition of up to 100 mg∙L(-1) β-alanine had no greater effect. The endogenous concentrations of β-alanine in fifty Cabernet Sauvignon grape samples exceeded the 1 mg∙L(-1) required for the stimulatory effect on ethyl and acetate ester production observed in this study.
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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".