Removal of Pesticide Residues from Okra Vegetable through Traditional Processing
Why this work is in the frame
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Bibliographic record
Abstract
Demand for vegetables in Pakistan is constantly increasing to feed growing population. Pakistan is the second largest producer of okra and in Sindh okra is produced throughout the year. Okra crop is attacked by variety of insect pests and commercial okra production relies heavily on the pesticides belonging to organochlorine, organophosphate, carbamate, pyrethroid and neo-nicotinoid groups for pest control. Moreover, growers do not observe safety interval for okra harvest. Hence the okra sold in Pakistani markets is highly contaminated with pesticide residues. Aim of this research study was to determine the extent of pesticide residue decontamination in okra vegetable through traditional processing. Okra crop was sprayed with bifenthrin, profenofos and endosulfan, and different processing were applied on okra such as washing, detergent washing, sun-drying and cooking, etc. Bifenthrin, profenofos and endosulfan pesticide residues were extracted from okra by solvent partitioning and cleaned up through Florisil column using organic solvents for elusion as described by EPA and FDA procedures. Cleaned up residues were analyzed through GC-µECD. The results revealed that endosulfan levels were reduced to MRL by detergent washing (from 2.01 ppm in unwashed samples to 1.03ppm). Profenofos residues (3.21ppm) were reduced to MRL (2.0ppm) by detergent washing and by combination of plain water washing and frying. Bifenthrin MRL is very low (0.04ppm) and only combination of detergent washing and frying reduced residues from 0.311 ppm to 0.042 ppm.
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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 | 0.001 | 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 | 0.001 |
| 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 it