Rapid and quantitative detection of multiple pesticide residues in orange with surface-enhanced Raman spectroscopy

Pesticide residues in crops are harmful to people’s health and have become a focal point that continues to be addressed. In this study, Silver Nanowires (AgNWs) were used as the surface-enhanced Raman spectroscopy (SERS) substrate to detect phosmet and dimethoate residues in oranges and achieved better results. The acquired spectral data were subsequently modeled based on partial least squares methods. For standard solutions of phosmet with concentrations ranging from 1 to 35 mg/l, the modeling effect was better after multiple scattering correction (MSC) preprocessing, where the correlation coefficient (Rp) was 0.967; for standard solutions of dimethoate, the Rp was 0.956 after smoothing and first derivatives preprocessing. For phosmet residues on the skin of navel oranges, which had a concentration of 5 to 35 mg/l, the modeling effect was better after second derivative preprocessing, where the Rp was 0.930 and the root mean square error of prediction (RMSEP) was 3.169 mg/l; for dimethoate residues on the skin of navel oranges, the Rp was 0.957 and the RMSEP was 2.969 mg/l after MSC preprocessing. To determine the accuracy of the modeling, the spectral data at the locations of the characteristic peaks were modeled. After intercepting the spectra of the phosmet-dependent characteristic peak, the modeling effect was better after MSC preprocessing, where the Rp was 0.911 and the RMSEP was 4.317 mg/l. After preprocessing the spectra of dimethoate through the second derivatives, the model had an Rp of 0.853 and an RMSEP of 5.159 mg/l. Analytical modeling results show that phosmet residues on oranges could be detected using SERS coupled with AgNWs, and the high sensitivity and rapidity of this method make it promising for food safety applications.