Glycosylation of Smoke-Derived Volatile Phenols in Grapes as a Consequence of Grapevine Exposure to Bushfire Smoke
Bibliographic record
Abstract
The presence of glycosides of smoke-derived volatile phenols in smoke-affected grapes and the resulting wines of Chardonnay and Cabernet Sauvignon was investigated with the aid of high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). All volatile phenols studied (phenol, p-, m-, and o-cresols, methylguaiacol, syringol, and methylsyringol) could be detected as glycosylated metabolites in smoke-affected grapes in a similar fashion to that previously reported for guaiacol. These phenolic glycosides were found in smoke-affected grapes and wines at significantly elevated levels compared to those in non-smoked control grapes and wines. The extraction of these glycosides from grapes into wine was estimated to be 78% for Chardonnay and 67% for Cabernet Sauvignon. After acid hydrolysis, a large proportion of these phenolic glycosides in grapes (50%) and wine (92%) disappeared but the concentrations of volatile phenols determined by gas chromatography-mass spectrometry (GC-MS) were lower than expected. In the case of wine, the majority of the glycosides of phenol, cresols, guaiacol, and methylguaiacol were decomposed upon acid hydrolysis without releasing their respective aglycones, while syringol and methylsyringol were more effectively released.
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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 | 0.000 | 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".