Application of Particle Swarm Optimization (PSO) Algorithm to Determine Dichlorvos Residue on the Surface of Navel Orange with Vis-NIR Spectroscopy

AbstractThere is increased interest in the investigation and implementation of rapid, non-destructive methods for detection of pesticide residues. This study reports development of a method for determination the pesticide concentration on the surface of fruit samples by using visible near-Infrared (Vis-NIR) spectroscopy. A total of 330 navel oranges were selected for the calibration (n=220) and prediction (n=110) sets, and sprayed dichlorvos solution diluted with tap water on the surface of fruit samples. After 12 h, the spectra of fruit samples were acquired in the range of 350-1800 nm, then pesticide residue was measured using gas chromatography (GC). Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of pesticide residue. The particle swarm optimization (PSO) was used for wavelengths selection of Vis-NIR spectra data in order to develop the PLS prediction model. The model using optimal intervals for predicting the concentration of dichlorvos residue selected by PSO algorithm yielded better results than the PLS model using full wavelengths. This PSO-PLS model could predict dichlorvos residue of 220 samples of prediction set with correlation coefficient of 0.8732. The wavelength selection through a PSO algorithm would enhance the predictive ability when applying PLS model