Optimization and validation of liquid-liquid extraction with partition at low temperature to determine the herbicide halauxifen-methyl in water
Bibliographic record
Abstract
The halauxifen-methyl, a pre-emergent herbicide of the arylpicolonate chemical group and with a mode of action as an auxin mimic, was authorized in Brazil in 2021 to combat weeds in corn and soybean crops. In other countries such as Canada, Australia, and the United States, this herbicide is also used in crops of various cereals. To date, there are few studies on methodologies for quantifying this herbicide in water. The present study aimed to optimize and validate liquid-liquid extraction with low-temperature partitioning (LLE-LTP) to determine the herbicide halauxifen-methyl in water, using high-performance liquid chromatography with diode array detection (HPLC-DAD). Initially, a standard working solution at 4 mg L⁻¹ was prepared and analyzed in an absorption spectrophotometer in the ultraviolet and visible regions, to identify the wavelengths of maximum absorbance. Then, the chromatographic conditions were evaluated, including wavelength, mobile phase flow, chromatographic column, column temperature, and injection volume. The parameters of the LLE-LTP technique were adjusted, considering the composition of the extracting phase, freezing time and ionic strength. The method was then validated according to the SANTE protocol. In the photodegradation analysis, 4 mL of water collected from a river were added to each of the 48 vials, fortifying the water to a concentration of 120 μg L⁻¹. Part of the samples was exposed to sunlight, while another part was protected with aluminum foil and stored in a sealed box. The samples were subjected to extraction and chromatographic analysis every five days, with two analyses performed every ten days. The results indicated that the ideal chromatographic conditions were established with a wavelength of 250 nm, a mobile phase of acetonitrile and water in a ratio of 80:20 v/v, Kinetex column, flow rate of 0.2 mL min⁻¹, column temperature of 30 °C and injection volume of 10 μL. The optimal extraction conditions included 7.5 mL of acetonitrile, 0.5 mL of dichloromethane, 90 min of freezing and 0.2 g of NaCl. Method validation revealed that LLE-LTP was selective, with a limit of quantification of 0.448 μg L⁻¹. The linearity range was observed between 0.448 μg L⁻¹ and 120 μg L⁻¹, with precision confirmed by means of the relative standard deviation between 2.40% and 11.51%, and accuracy validated by means of recovery rates between 86.60% and 110.91%. The matrix effect found was -11.19%. The photodegradation study of this herbicide in aqueous medium revealed that the half-life under these conditions was 6 days and the degradation under sunlight in approximately 50 days. The proposed method showed efficiency in monitoring halauxifen-methyl, with a value obtained in the quantification below the maximum residue limit established for water in herbicides of the same class. Furthermore, the result obtained in this study was similar to the method proposed in the literature for this herbicide and this matrix.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".