Glycosidically-Bound Volatile Phenols Linked to Smoke Taint: Stability during Fermentation with Different Yeasts and in Finished Wine
Bibliographic record
Abstract
When wine grapes are exposed to smoke, there is a risk that the resulting wines may possess smoky, ashy, or burnt aromas, a wine flaw known as smoke taint. Smoke taint occurs when the volatile phenols (VPs) largely responsible for the aroma of smoke are transformed in grape into a range of glycosides that are imperceptible by smell. The majority of VP-glycosides described to date are disaccharides possessing a reducing β-d-glucopyranosyl moiety. Here, a two-part experiment was performed to (1) assess the stability of 11 synthesized VP-glycosides towards general acid-catalyzed hydrolysis during aging, and (2) to examine whether yeast strains differed in their capacity to produce free VPs both from these model glycosides as well as from grapes that had been deliberately exposed to smoke. When fortified into both model and real wine matrices at 200 ng/g, all VP-disaccharides were stable over 12 weeks, while (42–50 ng/g) increases in free 4-ethylphenol and p-cresol were detected when these were added to wine as their monoglucosides. Guaiacol and phenol were the most abundantly produced VPs during fermentation, whether originating from natural VP-precursors in smoked-exposed Pinot Noir must, or due to fortification with synthetic VP-glycosides. Significant yeast strain-specific differences in glycolytic activities were observed for phenyl-β-d-glycopyranoside, with two strains (RC212 and BM45) being unable to hydrolyze this model VP, albeit both were active on the guaiacyl analogue. Thus, differences in Saccharomyces cerevisiae β-glucosidase activity appear to be influenced by the VP moiety.
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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 | 0.001 |
| 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 | 0.001 | 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".