Nondestructive Detection of Chlorpyrifos in Apples Based on Surface Enhanced Raman Scattering

A new non-destructive, rapid and accurate surface-enhanced Raman spectroscopy (SERS) assay method using silver colloidal nanoparticles for the detection of chlorpyrifos pesticides in apples is developed and optimized Silver colloidal nanoparticles are paperad by reduction of silver nitrate with hydroxylamine hydrochloride and investigated using scanning electron microscope and visible-ultraviolet spectroscopy. Trace amounts of acetone, silver colloid, and nitric acid are dropped onto apple samples and air exposed for 20 s. The SERS spectra are collected non-destructively from the apple sample with a self-developed Raman system. The relative standard deviation (RSD) is calculated to evaluate the stability of the present method, including the stability of silver colloid and the stability of the Raman system, which indicates that the proposed strategy has good stability. Secondary derivative transformation and min-max signal adaptive zooming method are used to remove the fluorescence background for eliminating the environmental impact and improving the accuracy of SERS results. Raman spectral signals are collected from 30 points on each sample with 450 mW laser power and 3 s exposure time. Chlorpyrifos concentrations in 41 samples are determined with gas chromatography after SERS spectra taken. The characteristic peaks of chlorpyrifos at 621 cm(-1) and 680 cm(-1) are visible even at the concentration of 0.064 mg/kg. Under optimal conditions, linear regression models are established between the SERS signal and the chlorpyrifos concentrations in the range of 15.52 similar to 0.064 mg/kg. There are good linear relationships between the concentrations of chlorpyrifos pesticides in apples and the Raman intensities of its two major characteristic peaks, for the best of which the correlation coefficient of prediction (R-p) is 0.969 and the root mean square error of prediction (RMSEP) is 1.24 mg/kg. The present study provides a potential method for the rapid detection of pesticides which can help to strengthen the safety and quality of our food supplies.