Rapid Detection of Imidacloprid in Apple Juice by Ultraviolet Spectroscopy Coupled with Support Vector Regression and Variable Selection Methods

Abstract

The widespread use of pesticides poses many potential risks to food safety and human health. Thus, rapid and accurate detection methods for pesticide residues need to be established. In this study, ultraviolet (UV) spectroscopy coupled with support vector regression and variable selection methods was used to quantitatively detect the content of imidacloprid in apple juice. First, the UV spectra of diff erent imidacloprid concentrations in apple juice were collected, and the acquired spectra were preprocessed by Savitzky–Golay smoothing. Then, the feature variables were selected by the variable iterative space shrinkage approach (VISSA), iteratively retains informative variables (IRIV), and random frog (RF) algorithms. Finally, particle swarm optimization support vector regression (PSOSVR) prediction models based on the feature variables and the full-spectrum variables were established to detect imidacloprid in apple juice. The results showed that the VISSA–PSO-SVR model had the optimal predictive performance, the determination coefficient of the prediction set (R_p²) was 0.99933, and the root mean square error of the prediction set (RMSEP) was 0.0894 mg/L. The results from this study indicated that the combination of UV spectroscopy and the VISSA–PSO-SVR model could be used for the quantitative detection of imidacloprid in apple juice.