Influence of Fruit Maturity at Harvest on the Intensity of Smoke Taint in Wine
Bibliographic record
Abstract
Bushfire smoke can affect the composition and sensory properties of grapes and wines, in some cases leading to wines which exhibit undesirable "smoky", "ashy" and "medicinal" characters. This study investigated the extent to which fruit maturity (i.e., ripeness) influences the perception of smoke taint in wine. Two white grape varieties (Chardonnay and Sauvignon Blanc) and two red grape varieties (Merlot and Shiraz) were exposed to smoke under experimental conditions, at approximately seven days post-veraison. Fruit was then harvested at two levels of maturity: Harvest A, when total soluble solids were 16-20 °Brix, i.e., the berry ripeness typically required for production of sparkling or light-bodied wines; and Harvest B, when total soluble solids were 22-25 °Brix, i.e., the berry ripeness typically required for production of full-bodied wines. The intensity of smoke taint in resulting wines was found to be influenced by fruit maturity, but differed between grape varieties. Smoke-related sensory attributes were apparent in Sauvignon Blanc wine made from early-harvested fruit and in Chardonnay wine made from late-harvested fruit, only; whereas Merlot and Shiraz wines exhibited smoke taint irrespective of fruit maturity. Smoke-derived volatile phenols, and various alcohols, esters and acids, were also quantified to determine the impact of smoke exposure and fruit maturity respectively, on wine composition.
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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 | 0.000 | 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 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".