Chemometric Approach to Pesticide Residue Analysis in Surface Water

Abstract

Dimethachlor is an herbicide used for oilseed rape protection. Previous studies have demonstrated its high mobility in the soil, which could lead to water contamination. This research aimed to determine the occurrence of dimethachlor and its metabolites (dimethachlor ethanesulfonic acid ESA and dimethachlor oxalamic acid OA) in surface water using a recently developed analytical method. This article is one of the first to document dimethachlor and its metabolites’ presence in surface water samples. The samples were collected from the Danube river and Tisza river. The quantitative determination of dimethachlor and its metabolites in the obtained extracts was done by high-performance liquid chromatography. Descriptive statistical methods, including correlation analysis, cluster analysis, and principal component analyses, were utilized to analyze method validation experimental results. In addition, the artificial neural network (ANN) model was applied as an optimization tool. The developed ANN model adequately predicted observed variables, suggesting the optimum results were obtained at a pH value 7, spike value 1, and injection volume equal to 0.5 μL. The average concentrations in Danube River samples were 1.51 µg/L for OA 0.01 µg/L for ESA, and 0.63 µg/L for DMC, while the average concentrations of chloroacetanilide herbicides detected in Tisza River samples were 1.43 µg/L for OA, 0.08 µg/L for ESA and 1.82 µg/L for DMC.

Camelina sativa Oilseed Cake as a Potential Source of Biopolymer Films: A Chemometric Approach to Synthesis, Characterization, and Optimization

“…positive correlations, and red was used to represent negative correlations. The strength o these relationships was represented by the size of the circles in the diagram [85]. The cor relation analysis revealed statistically significant relationships (p < 0.001) between S and AO.…”

The Influence of Biopolymer Coating Based on Pumpkin Oil Cake Activated with Mentha piperita Essential Oil on the Quality and Shelf-Life of Grape

“…For the ANN model, the model calculated TNAB, Yeasts and Molds, were not too close to the experimental values in most cases in terms of r 2 values, while the sum of squares (SOS) values acquired using the ANN model were of the same order of magnitude as experimental errors for the outputs mentioned in the literature [58][59][60].…”

Heavy metal concentrations in the soil near illegal landfills in the vicinity of agricultural areas—artificial neural network approach

“…A multi-layer perceptron (MLP) structure, including three layers (input, hidden, and output), was used for modeling eight ANN models for the prediction of As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg concentrations in the soil near illegal landfills in the vicinity of agricultural areas based on landfill size, municipality size, the number of residents, plant species, soil, and landforms types. According to the known references, the ANN models were confirmed as completely fitted to evaluating nonlinear functions (Ćurčić et al 2022;Ruškić et al 2022). Input data were continuously introduced to the network; however, before calculation, the input and output database was normalized according to the min-max formula to enhance the behavior of the ANN models (Rajković et al 2022).…”