Detection of pesticides on navel orange skin by surface-enhanced Raman spectroscopy coupled with Ag nanostructures

Residual pesticides such as phosmet and chlorpyrifos in fruit have become a public concern problem in recent years. In this study, surface-enhanced Raman spectroscopy (SERS) was used to detect and characterize pesticides extracted from navel orange surfaces. Silver colloid was prepared for getting the SERS of phosmet and chlorpyrifos. Enhanced Raman signals of phosmet over a concentration range of 5 mg/L to 30 mg/L and chlorpyrifos over a concentration range of 5 mg/L to 20 mg/L were acquired. Partial least squares (PLS) regression combined with different data preprocessing methods was used to develop quantitative models. With the second derivative data preprocessing, the best prediction model of phosmet pesticide was achieved with a correlation coefficient (r) of 0.852 and the root mean square error of prediction (RMSEP) of 5.177 mg/L. The best prediction model of chlorpyrifos pesticide was achieved with r of 0.843 and the RMSEP of 2.992 mg/L using the multiplicative scatter correction (MSC) and first derivative data preprocessing. This study indicated that SERS coupled with Ag nanostructures is a potential tool for analysis of phosmet and chlorpyrifos pesticide residues.