Modelling Smoke Flavour in Wine from Chemical Composition of Smoke-Exposed Grapes and Wine
Bibliographic record
Abstract
Wine grapes exposed to smoke and wine made from grapes exposed to smoke can robustly be identified through their elevated concentrations of volatile phenols and phenolic glycosides serving as smoke markers, compared to concentrations typically found in non-smoke-exposed samples. Smoke-affected wines with high concentrations of volatile phenols and glycosides can have smoky flavours, but the relationship between concentrations of specific smoke markers in grapes and the intensity of smoky sensory attributes in the resulting wine has not been established. This study sought to determine whether volatile phenols and glycoside concentration in grapes and wine are suited to predict smoke flavour, to identify the key drivers of smoke flavour in both matrices. The study aimed to determine what concentrations of volatiles and glycosides in grapes impart an unacceptable smoke flavour in the resulting wine, to provide a guide for producers assessing suitability of smoke-exposed grapes for wine production. During vintage 2020, a total of 65 grape samples were collected from vineyards exposed to bushfire smoke, as well as unaffected vineyards. Chardonnay, Pinot Noir, and Shiraz grapes were harvested from vineyards in New South Wales, South Australia, and Victoria. Unoaked wines (50 kg scale) were produced under controlled conditions. The wines had a wide range of smoke flavour intensities rated by a trained sensory panel. Statistical models based on guaiacol, o-cresol, m-cresol, p-cresol, and some glycosides gave good predictions of smoke flavour intensity, with a slightly different optimal model for each cultivar. Subsequently, critical concentrations for quality defects were estimated to provide a guide for producers. A subset of smoke exposure markers in wine grapes affected by smoke from bushfires can be used to predict the degree of smoke flavour in wine. This information provides a first guide for assessing the risk of producing smoke tainted wine from smoke-exposed grapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".