Pesticide Residues in Unfermented Grape Juices and Raw Wines: A 5-year Survey of More than 3000 Products
Bibliographic record
Abstract
The concentrations of 26 pesticides have been assayed in 1537 wines and in 1827 raw juices prior to fermentation from nine major wine-producing countries or regions. Seventeen pesticides were measurable in < 1% of the wines and juices. Seven yielded measurable concentrations in > 1% (18) juice products, three of which were also found in > 1% (15) of the wine samples. With the exception of Carbaryl (37 samples, 2.4%), no pesticide exceeded a concentration of 0.1 mg l -1 in > 0.5% (eight) of the wines analysed; indeed, this occurred for all other pesticides in only seven wines (0.47% of the total). In the case of juices, concentrations > 0.1 mg l -1 were detected most frequently for Folpet (219 samples, 12%) and Captan (212 samples, 11.6%) and in > 1% of samples for Guthion, Imidan and Carbaryl. The principles of good manufacturing practice suggest that maximal residual levels (MRLs) for Carbaryl in wine should be set at 0.8mg l -1 and 0.1 mg l -1 for all other pesticides. The same principles are consistent with MRLs of 0.5 mg l -1 for all except Folpet and Captan in juices. The latter two pesticides as parent compounds are rarely found in wines, even in trace amounts, but the presence of their metabolites has been well documented, and should be regarded with caution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".