Effective removal of organophosphorus pesticide residues in tomatoes using natural extracts
Bibliographic record
Abstract
Abstract The reduction efficiency of the extracts of Albizia amara and Acacia concinna against most widely used organophosphorus pesticides such as dimethoate, malathion, dichlorvos, and chlorpyrifos on tomato samples was investigated. The pesticide residues were quantified by gas chromatography with triple quadrupole mass spectrometer (GC‐MS/MS). The maximum reduction efficiency was achieved from the samples treated with 8% A. concinna extract on dichlorvos (87%) and dimethoate (84%) followed by 6% A. amara on malathion (83%) and chlorpyrifos (64%) and comparatively higher than water wash and it shows the reduction of only 14–38% for 15 min. The sensorial property and lycopene content of tomato samples were assessed and these plant extracts showed no significant effects on color, texture, and lycopene content after washing treatment. These phytochemical rich plant extracts exhibited greater reduction ability against pesticide residues without changing the nutritional and sensorial characteristics of the treated tomato samples. The current study may be a platform for formulating a cost‐effective, safe, and natural cleanser for the effective removal of pesticide residues in fruits and vegetables. Practical applications Presence of multiple pesticide residues in fruits and vegetables is a worldwide problem in food safety. Particularly, tomatoes are most often consumed without cooking, hence it is essential to estimate the pesticide reduction efficiency of the common washing procedure. Washing of tomatoes with chemical solutions to remove pesticide residues leads to additional chemical exposure. Therefore, the reduction efficiency of natural plant extracts such as Albizia amara and Acacia concinna on four pesticides (dimethoate, malathion, dichlorvos, and chlorpyrifos) was studied. The method for the analysis of pesticide using GC‐MS/MS was validated according to SANTE 11813/2017 to detect the pesticide residues in tomatoes after each washing treatment. This study showed that the natural extracts could be a good tool for the removal of multipesticide residues effectively from the tomatoes without compromising its nutritional and sensory properties. The pesticide decontamination with plant extracts was effective, economic and safe for household and commercial applications.
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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".