Guaiacol and 4-methylguaiacol accumulate in wines made from smoke-affected fruit because of hydrolysis of their conjugates
Bibliographic record
Abstract
Background and Aims: Taint in smoke-exposed grapes have been associated with elevated levels of guaiacol and 4-methylguaiacol. Previous research has reported guaiacol and 4-methylguaiacol in both fruits and wines. In some cases, these compounds were not detected, or were detected at low levels in the fruit while high levels were subsequently identified during or after winemaking. Later research indicated that this was due to the presence of glycosidic conjugates. Here we report a method for the routine analysis of guaiacol and 4-methylguaiacol released after acid hydrolysis of glycoside precursors. Methods and Results: Chardonnay, Merlot, Shiraz, Sangiovese and Cabernet Sauvignon fruits were collected following bushfire events in 2006–2007 in the King Valley wine region of NE Victoria, Australia. Gas chromatography-mass spectrometry (GC-MS) was used to detect free guaiacol and 4-methylguaiacol in both fruits and wines. Low levels of free and bound forms were present in fruit not exposed to smoke. Substantial levels of free guaiacol and 4-methylguaiacol were detected in the wines made from the smoke-affected fruits. These compounds increased during bottle storage. Acid hydrolysis of wines and berries resulted in a several-fold increase in free guaiacol and 4-methylguaiacol. Conclusions: The validated GC-MS method is suitable for monitoring free and glycosidically bound guaiacol and 4-methylguaiacol after acid hydrolysis in both fruits and wines. Acid hydrolysis of wines provided evidence that bound volatiles, most probably glycosidically, act as reserve for guaiacol and 4-methylguaiacol, which are released during ageing of wines. Significance of the Study: This is the first study published in a refereed journal to demonstrate that smoke taint-associated volatiles increase during ageing of wine and bound forms of guaiacol and 4-methylguaiacol represent an aroma reserve for smoke taint in ageing/bottled wines.
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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 | 0.001 | 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) | 0.001 | 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".