Optimization of DLLME Extraction Parameters for Pesticides Analysis by High Performance Liquid Chromatography
Bibliographic record
Abstract
Pesticides have been used widely all over the world for centuries to increase agricultural production and to combat harmful pests.Pesticides used for agriculture, household, and public health sectors eventually reach water bodies, posing a risk to human health and the environment even in micro concentrations.Each country legally sets its own maximum allowable limits to regulate pesticides residues.Therefore, it is necessary to monitor pesticide residues in water resources, especially if they are used for drinking water purposes.With the technological developments, nowadays sensitive analytical instruments employing complex analysis methods such as GC/MS and LC/MS have been used to analyze pesticides.Pesticide residues must be concentrated by different extraction methods before HPLC analysis to be able to detect them in micro levels with high precision and low detection limits.The aim of this study is to develop an analytical method for the analysis of acetochlor and metolachlor pesticides by HPLC instrument with dispersive liquid-liquid micro extraction (DLLME) method.In order to determine optimum conditions for DLLME extraction method, extraction solvent type (chloroform, dichloromethane and 1,2 dichloroethane), dispersive solvent type (acetonitrile, methanol and isopropyl alcohol), flowrate (1, 1.2 and 1.5 ml/min), oven temperature (20°C, 30 °C, 40 °C and 50 °C), volume of extraction solvent (300, 350 and 400 µl), and mobile phase mixture (60/40, 70/30 and 80/20 acetonitrile/water in v/v) were comprehensively investigated with Taguchi experimental design.The optimized conditions for acetochlor and metolachlor were obtained as: extraction solvent of 1,2-dichloroethane, dispersive solvent of acetonitrile and methanol, extraction solvent volume of 400 µL and 300 µL, dispersive volume of 1 ml, flowrate of 1 ml/min and 1.2 ml/min, temperature of 40°C and 50°C and mobile phase mixture of 70/30 v/v and 80/20 v/v, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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) | 0.001 | 0.002 |
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".