Rapid Detection of Pesticide Residues in Leaf Vegetables by SERS Technology.

Organophosphate pesticides, fungicides, and neonicotinoid insecticides are frequently employed in the cultivation and production of leafy vegetables. The conventional detection methods for these pesticides rely on chromatographic techniques, which are characterized by good precision and sensitivity. Nevertheless, these methods suffer from drawbacks such as complex sample pretreatment, prolonged detection times, and high costs, hindering the realization of on-site detection. This paper introduces a detection method based on surface-enhanced Raman spectroscopy (SERS) for the quantitative and qualitative analysis of pesticide residues in leafy vegetables. Gold nanoparticles (AuNPs) were meticulously synthesized to serve as the substrate for enhancing Raman signals. The average particle size was approximately 50 nm, and a significant absorption peak appeared at 536 nm. The density functional theory (DFT) with the B3LYP/6-311G was utilized to calculate the theoretical Raman spectra of the pesticides. The characteristic Raman peaks of the pesticides were selected as calibration peaks to establish calibration equations relating the concentration of pesticide residues to the intensity of these calibration peaks. By substituting the intensity of the calibration peak corresponding to the lowest detectable limit in the SERS spectra into the calibration equation, the quantitative detection limit was calculated. The study revealed that the detection limit for phosmet residues in Chinese cabbage could be was below 0.5 mg/kg, with an R of 0.93363, a standard deviation ranging from 3.87% to 8.56%, and recovery rates between 94.67% and 112.89%. For thiabendazole residues in water spinach, the detection limit could be below 1 mg/kg, with an R of 0.98291, a standard deviation of between 1.71% and 9.29%, and recovery rates ranging from 87.67% to 107.83%. In the case of acetamiprid residues in pakchoi, the detection limit could also be below 1 mg/kg, with an R of 0.95332, a standard deviation of between 4.00% and 9.10%, and recovery rates ranging from 90.67% to 113.75%. These findings demonstrate that the SERS-based detection method for the semi-quantitative and qualitative analysis of pesticide residues in leafy vegetables is an effective approach, enabling rapid and reliable detection of pesticide residues in leafy vegetables.