Simultaneous Detection of Imidacloprid, Acetamiprid, and Clothianidin in Apple Juice Using Ultraviolet Spectroscopy and the SSA–BPNN Model

Abstract

The extensive use of pesticides can pose potential risks to food safety and human health. Therefore, establishing simple and effective detection methods for pesticide residues is necessary. In this study, ultraviolet (UV) spectroscopy is used for the simultaneous detection of imidacloprid, acetamiprid, and clothianidin in apple juice. A total of 84 samples with different concentrations of imidacloprid, acetamiprid, and clothianidin in apple juice are prepared, and the corresponding UV spectra are collected using a UV-visible spectrophotometer. The quantitative detection model is built using the back propagation neural network (BPNN), and the BPNN model is optimized using the sparrow search algorithm (SSA). To verify the performance of the model, the prediction results of the SSA−BPNN model are compared with those of the extreme learning machine (ELM), BPNN, and particle swarm optimization back propagation neural network (PSO–BPNN) models. The results show that UV spectroscopy coupled with the SSA–BPNN model is effective for the simultaneous detection of imidacloprid, acetamiprid, and clothianidin in apple juice; this method is important for ensuring food safety and human health.