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 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 | 0.001 |
| 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".