Electronic tool coupled with machine learning algorithms for the detection of deltamethrin residues in Mentha Spicata L.

Abstract

Insecticide residues in food are a real danger that threatens the health of living beings and therefore human health. In the present study, we propose an electronic nose system that performs collection, pre-treatment, and processing as well as data analysis and consequently decision-making to judge the presence of insecticide residues in the agricultural product. The present case is reserved for the evaluation of the presence of deltamethrin residues in the mint. The system is composed of two parts, the hardware part based on an array of commercial gas sensors, and the software part where we use some machine learning algorithms, and for the appropriate choice of the suitable algorithm, an investigation will be done. To decide the mint type (treated or untreated), several machine learning classifiers with 5-fold cross-validation were evaluated to know support vector machines (SVM), k-nearest neighbors (KNN), naïve Bayes (NB), and decision trees (DT). Concerning the top results, a success rate of 95% was attained by the SVM. Accordingly, it can be said that great results can be obtained by designing and implementing an adequate gas sensor array system, as well as selecting the appropriate machine learning classifier.