Pesticide Residues Detection in Agricultural Products

Abstract

Abstract Pesticides have been the most often used substance in recent decades to protect agricultural goods from pests affecting farmers, especially in conventional agriculture. Pesticides are effective in preventing and removing pests. On the other hand, pesticides risk human health since they may be found in agricultural goods for an extended time. As a result, it is critical to have a robust analytical procedure in place to monitor pesticide residues in agricultural products. Chromatography, Raman spectroscopy, and Ultraviolet-visible (UV-VIS) - Near Infrared (NIR) are methods used to identify pesticide residues, and each has benefits. Additionally, a cutting-edge technique called hyperspectral imaging has recently been employed. This review paper discusses the most current application of those approaches, combined with machine learning and chemometrics, in identifying pesticide residues in agricultural goods such as crops, vegetables, and fruits. The approach's basic principles, benefits, and drawbacks will be briefly addressed. Our findings indicate that those methods provide precise and stable results for identifying pesticide residues in agricultural products. However, most of those methods are possessed a high initial cost, complex processes, time-consuming, which is inappropriate with the agricultural modern concept, especially related to smallholder farmers. Hence, shortly, a low-cost, portable, and highly accurate internet-connected device must be developed. Keywords: Pesticide residue, Chromatography, Raman spectroscopy, UV-VIS-NIR spectroscopy, Hyperspectral imaging