Optimization and Application of the QuEChERS-UHPLC-QTOF-MS Method for the Determination of Broflanilide Residues in Agricultural Soils.

In this study, the separation conditions of UHPLC-QTOF-MS and the extraction conditions of QuEChERS were optimized. The analytical process for determining Broflanilide residues in different soil types was successfully established and applied to its adsorption, desorption, and leaching in soil. Broflanilide was extracted from soil with acetonitrile and purified using PSA and MgSO4. The modified UHPLC-QTOF-MS method was used for quantification. The average recovery of Broflanilide was between 87.7% and 94.38%, with the RSD lower than 7.6%. In the analysis of adsorption, desorption, and leaching quantities in four soil types, the RSD was less than 9.2%, showing good stability of the method, which can be applied to determine the residue of Broflanilide in different soils.

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm.

Selenium constitutes an essential trace element for the human body. Moderate Se intake plays a pivotal role in preserving overall health. The absorption of Se by plants is primarily influenced by the available Se levels in soils, rather than by the soil total Se content, offering potential for exploring Se-rich crops in Se-deficient regions. In this study, we explore the factors influencing the Se bioaccumulation coefficient in corn based on a land quality geochemical survey at a 1:50,000 scale and establish predictive models for corn seed Se content using random forest and multiple linear regression approaches. The results indicate that the surface soil in the study area is deficient in Se (0.18-1.21 mg/kg), but 54% of the corn grain samples met the standards for Se-rich products (0.02-0.30 mg/kg). The factors influencing the Se biological enrichment coefficient in corn seeds are soil pH and CaO and MgO content, with impact levels of 0.54, 0.42, and 0.35, respectively. Compared to multiple linear regression models, the RF model provides more accurate and reliable predictions of corn Se content. The random forest model indicates that approximately 41% of the farmland within the study area is conducive to the cultivation of naturally Se-rich corn, which is a 26% increase in the planting area compared to recommendations based solely on soil Se content. In this research, we introduce an innovative methodological framework for organically cultivating naturally Se-rich corn within regions affected by Se deficiency.