Novel use of activated carbon fabric to mitigate smoke taint in grapes and wine
Bibliographic record
Abstract
Background and Aims Grapegrowers and winemakers, around the world, are searching for strategies to mitigate the compositional and sensory consequences of grapevine exposure to smoke from wildfires. This study evaluated the use of activated carbon fabrics as protective coverings to mitigate the uptake of smoke-derived volatile phenols by grapes, and accordingly, the intensity of smoky, ashy characters in wine. Methods and Results Smoke was applied to Mataro grapes, with and without individual bunches being enclosed in bags made from three activated carbon fabrics (felt, light cloth and heavy cloth). Wine made from smoke-exposed grapes had an elevated concentration of volatile phenols, but the composition of wines made from grapes protected by activated carbon fabric was comparable to that of the Control wine; the difference in concentration of guaiacol, o- and m-cresol and/or syringol was only 1 μg/L. Wine made from smoke-exposed grapes had diminished fruit and prominent smoke characters, whereas the sensory profile of the wines corresponding to activated carbon fabric treatments could not be differentiated from that of the Control wine. Analysis by GC/MS of the activated carbon fabrics following repeated smoke exposure confirmed their adsorption of smoke volatiles. Conclusions The activated carbon fabrics successfully protected Mataro grapes and wine from being tainted by smoke exposure. Significance of the Study This study demonstrates a promising new technology for overcoming smoke taint, an issue of major concern for grape and wine producers worldwide.
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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".