Amelioration of smoke taint in wine by treatment with commercial fining agents
Bibliographic record
Abstract
Background and Aims: Fermentation of smoke-affected grapes can lead to wines that exhibit objectionable smoke-related sensory attributes, i.e. smoke taint. Fining agents are routinely used at different stages of the winemaking process to address constituents that are considered to adversely affect juice or wine quality. This study aimed to evaluate the efficacy of commercial fining agents in reducing the concentration of volatile phenols and the intensity of sensory attributes associated with smoke-tainted wine. Methods and Results: Smoke-affected wines were treated with a range of fining agents, two of which, an activated carbon and a synthetic mineral, were found to appreciably ameliorate the taint. Treated wines contained a significantly lower level of smoke-derived volatile phenols and exhibited less intense ‘smoke’ and ‘cold ash’ aromas, ‘smoky’ flavour and ‘ashy’ aftertaste, compared with that of untreated (control) wines; with little or no impact on wine colour. Conclusions: Selected fining agents can ameliorate smoke taint in wine. Whereas most fining agents showed poor specificity towards the wine components responsible for smoke taint, some, an activated carbon in particular, were highly effective. Significance of the Study: This research identifies a treatment that can be used to mitigate the impact of grapevine exposure to smoke on wine composition and sensory properties.
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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".