Processing Factors of Several Pesticides and Degradation Products in Carrots by Household and Industrial Processing
Bibliographic record
Abstract
To quantify the effect of household and industrial processing on the pesticide residues, carrots (Daucus carota) were sprayed during cultivation with three fungicides (boscalid, difenoconazole and tebuconazole), two insecticides (chlorpyrifos and dimethoate) and one herbicide (linuron). The most concentrated formulations were applied pursuant to Good Agricultural Practices, to ensure sufficiently high levels of residues, The subsequent processing conditions were established to correspond as close as possible to the actual conditions that are normally used in industrial practice. The effects of household and industrial processing on the levels of the six pesticide residues and eight associated degradation products were quantified. The washing step allowed decreasing the concentration of residues for all pesticides up to ~ 90%. It was the most effective step to remove pesticide residues from carrots. The second process, peeling, results in a reduction comparable to washing. The blanching step, combining heat with a large quantity of water, enhanced the elimination of residues (maximum 50%). After cutting and washing, the residual concentrations were below 5 ppb. However it was observed that the level of pesticide residues was not reduced by microwave cooking. The pesticide residues remaining after previous processing, except difenoconazole, were decreased by sterilization. The cumulative processes allowed the elimination of minimum 90% of pesticide residues. Degradation products, investigated in this study, were not detected.
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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 | 0.000 |
| 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 | 0.000 |
| 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".