A fluorescence probe-smartphone-machine learning integrated platform for the visual and intelligent detection of imidacloprid in agricultural products

Abstract

Imidacloprid is a pesticide commonly used in agriculture production. Portable and accurate detection of imidacloprid residues is of great significance to food safety and human health. Herein, a red-emitting rare earth complex (Eu-IMDC) probe is prepared, which features low detection limit (75 nM), high selectivity and fast response speed (30 s) for imidacloprid detection. The detection mechanism is investigated through experiments and theoretical calculations. In addition, an intelligent detection platform integrating the fluorescence probe, smartphone and a feedforward neural network (FNN) model is constructed and applied to imidacloprid detection in real rice, millet, and ginger samples, achieving recovery rates range from 96.78 % to 104.77 % and relative standard deviation (RSD) values below than 3.83 %. Meanwhile, relevant parameters, such as the coefficient of determination (R²), root mean square error (RMSE) and residual prediction deviation (RPD) values, indicating excellent fitting and predictive performance of the FNN model. This work offers a rapid, portable, and intelligent sensing platform for pesticide residues in agricultural products.