Ethyl carbamate content in wines with malolactic fermentation induced at different points in the vinification process
Bibliographic record
Abstract
Ethyl carbamate (EC) is a carcinogenic compound found in fermented food and beverages such as wine. Although its carcinogenic potential in animals is known, information regarding its effects in humans remains insufficient, thus there is increasing interest in its research. EC content is higher in products with high alcohol content and in aged products. The main precursor involved in EC production in wine is urea, which is produced by metabolism of arginine by yeast, but there is also evidence that EC levels can increase after malolactic fermentation (MLF). Some lactic acid bacteria (LAB) can degrade the arginine present in must and wine via the arginine deiminase pathway, producing citrulline and carbamyl phosphate. Both compounds can react with ethanol in acidic conditions and produce EC. Our research group is studying the influence of MLF induced at different points of wine-making on the quality of the resulting wine. Among other parameters, the content of toxic compounds such as EC was evaluated. Results so far indicate that EC levels at the end of MLF were quite low (less than 3 μg/l) in all cases, i.e. below the existing legal limit (e.g. 30 μg/l in Canada). In almost all wines, EC concentrations increased after 8 months of storage as has been described by other authors. In some of the wines in which MLF was carried out by selected LAB, the increase in EC concentration was lower.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".