Amelioration of smoke taint in wine by treatment with commercial fining agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it