Amelioration of smoke taint in wine by reverse osmosis and solid phase adsorption
Bibliographic record
Abstract
Background and Aims: Wines made from grapes harvested from vineyards exposed to bushfire smoke can exhibit objectionable ‘smoky’, ‘cold ash’, ‘medicinal’ and ‘ashy’ aroma and flavour characters. This study evaluated a combined reverse osmosis and solid phase adsorption process as a potential amelioration method for the treatment of smoke-tainted wines. Methods and Results: Smoke-tainted wines were treated using either pilot or commercial scale reverse osmosis systems and the chemical composition and sensory properties of wine compared before and after treatment. The concentrations of smoke-derived volatile phenols, including marker compounds, guaiacol and 4-methylguaiacol, decreased significantly with treatment. As a consequence, diminished smoke-related sensory attributes enabled treated wines to be readily differentiated from untreated wines. However, the taint was found to slowly return with time, likely because of hydrolysis of glycoconjugate precursors, which were not removed during the treatment process. Conclusions: Reverse osmosis and solid phase adsorption reduced the concentration of smoked-derived volatile phenols and improved the sensory attributes of smoke-tainted wines. Significance of the Study: This is the first study to evaluate the amelioration of smoke taint in wine using reverse osmosis and solid phase adsorption.
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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 | 0.001 |
| 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 | 0.001 |
| 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".