4-Ethylphenol and 4-ethylguaiacol in wines: Estimating non-microbial sourced contributions and toxicological considerations
Bibliographic record
Abstract
Analyses of commercially available wines suggested non-Brettanomyces sources of 4-ethylphenol and 4-ethylguaiacol. Grapes, enological additions, exposure to plastics, and oak-barrel aging were potential inputs considered. Investigations of whole grape bunch samples from two major red wine Vitis vinifera cultivars (L. cv. Cabernet Franc and Pinot Noir), a commercial mannoprotein additive, and three commercial enological tannin additions indicated they are not likely significant sources of these compounds. Studies on 15 commercial oak barrelled red wines from six Vitis vinifera cultivars (L. cv. Cabernet Franc, Cabernet Sauvignon, Dunkelfelder, Merlot, Pinot Meunier, and Pinot Noir), and a review of volatile phenol extraction from toasted oak wood, suggested that oak-aging may produce concentrations of up to 50 microg L(-1) 4-ethylphenol and 4-ethylguaiacol. Thus, following potential Brettanomyces-sourced aroma impacts in wine using 4-ethylphenol and/or 4-ethylguaiacol concentrations as proxies should only be considered reliable at analyte levels>100 microg L(-1). A review of worldwide 4-ethylphenol and 4-ethylguaiacol concentrations in wine, consumption patterns, and available toxicological data also suggested that levels of 4-ethylphenol being observed in wines worldwide do not warrant concerns about acute or long-term effects. While little is known about the toxicology of 4-ethylguaiacol, it is unlikely that elevated concentrations will pose any health-related concerns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".