Validation of QuEChERS method for the determination of 36 pesticide residues in fruits and vegetables from Ghana, using gas chromatography with electron capture and pulsed flame photometric detectors
Bibliographic record
Abstract
In this study, "Quick, Easy, Cheap, Effective, Rugged and Safe" 'QuEChERS' method was modified for the determination of 36 pesticides fortified at (0.01-1.0) mg kg(-1) in three vegetables and a fruit (lettuce, carrot, tomatoes and pineapples respectively) from Ghana. The method involved extraction with acetonitrile, phase separation with primary secondary amine and magnesium sulfate; the final injection solution was reconstituted in ethyl acetate. Organochlorine and synthetic pyrethroids residues were detected with electron capture detector whereas organophosphorus, pulsed flame photometric detector was used. The recoveries at different concentration levels (0.01, 0.1 and 1.0 mg kg(-1)) were in the range of 83% and 93% with relative standard deviation ranging from 2% to 10% (n = 5) and the coefficient of determination (R(2)) was greater than 0.99 for all the 36 pesticides. The method was successfully tested on 120 real samples from Accra markets and this proved to be useful for monitoring purposes particularly in laboratories that have no gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".