A detection method of two carbamate pesticides residues on tomatoes utilizing excitation-emission matrix fluorescence technique

Abstract

A mind evolutionary algorithm (MEA) originated from genetic algorithm (GA) used to optimize back propagation (BP) neural network (MEA-BP) was proposed to detect two carbamate pesticides residues on tomatoes by excitation-emission fluorescence. Two toxic insecticides (Tsumacide and Carbaryl) were used as representative carbamate pesticides. Through a series of evaluation experiments, the optimized method had obtained relatively satisfactory results by comparing three parameters including iteration time, correlation coefficient and mean square error, which were 304, 0.99873 and 0.0057. In terms of quantitative analysis, relative error and average recoveries of two pesticides were 1.325%, 2.375% and 98.94%, 99.25%, respectively. In addition, it had satisfactory precision repeatability. Based on these results, MEA-BP model was found to work better which indicated that it had high convergence speed and accuracy for detecting pesticides residues on tomatoes. Moreover, the best cleaning method was determined. Hopefully, this MEA-BP method could encourage further research for accurate measurement and provide reference opinions on removing pesticides residues on food surfaces in daily life.