Amelioration of Smoke Taint in Cabernet Sauvignon Wine via Post-Harvest Ozonation of Grapes
Bibliographic record
Abstract
Strategies that mitigate the negative effects of vineyard exposure to smoke on wine composition and sensory properties are needed to address the recurring incidence of bushfires in or near wine regions. Recent research demonstrated the potential for post-harvest ozonation of moderately smoke-exposed grapes to reduce both the concentration of smoke taint marker compounds (i.e., volatile phenols and their glycosides) and the perceived intensity of smoke taint in wine, depending on the dose and duration of ozone treatment. The current study further evaluated the efficacy of ozonation as a method for the amelioration of smoke taint in wine by comparing the chemical and sensory consequences of post-harvest ozonation (at 1 ppm for 24 h) of Cabernet Sauvignon grapes following grapevine exposure to dense smoke, i.e., ozone treatment of more heavily tainted grapes. Ozonation again yielded significant reductions in the concentration of free and glycosylated volatile phenols—up to 25% and 30%, respectively. However, although the intensities of smoke-related sensory attributes were generally lower in wines made with smoke-exposed grapes that were ozonated (compared to wines made with smoke-exposed grapes that were not ozonated), the results were not statistically significant. This suggests that the efficacy of ozone treatment depends on the extent to which grapes have been tainted by smoke.
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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) | 0.001 | 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".