Washing effects of limonene on pesticide residues in green peppers
Bibliographic record
Abstract
BACKGROUND: The presence of pesticide residues in food has caused much concern. The low health risks and environmental impacts of limonene make it a very interesting solvent for use in green chemistry. Washing effects of limonene on pesticide residues of methyl chlorpyrifos, chlorothalonil, chlorpyrifos, fenpropathrin and deltamethrin were investigated in green pepper. RESULTS: Results showed that washing with a low concentration of limonene for 5 min (where LOQ is limit of quantitation) caused 53.67%, <LOQ, 64.29%, 68.69% and 66.22% loss of the above pesticides, respectively, while corresponding values of washing with a high concentration were 84.64%, <LOQ, 90.46%, 89.00% and 89.36%, respectively. Washing with a low concentration of limonene for 10 min produced 55.90%, <LOQ, 66.19%, 72.08% and 73.25% loss, respectively, while corresponding values of washing with a high concentration were 94.42%, <LOQ, 96.58%, 92.04% and < LOQ, respectively. The reductions due to washing with tap water (for 10 min) and the emulsion with only egg yolk lecithin (at high concentration for 10 min) were 25.18 %, 37.83%, 21.84%, 20.87%, 13.86% and < LOQ, 59.70%, 54.09%, 54.76%, 54.47%, respectively. CONCLUSION: The data indicated that washing with a low concentration of limonene for 5 min was the optimal treatment for elimination of pesticide residues in green pepper, considering effect and treatment time as well as cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".