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Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice.

Surface-enhanced Raman spectroscopy (SERS) coupled with characteristic peaks screening methods was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. MgSO4, Primary secondary amine (PSA) and C18 were employed to purify the rice extraction. Successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six raman peaks such as 671, 834, 1016, 1114, 1436 and 1444 cm-1 were selected by SPA and SVM model had better performance using six peaks (only 0.92% of the full spectra variables) with R2p = 0.97, RMSEP = 2.89 and RPD = 4.26. And the experiment time for a sample was accomplished within 10 min. Recovery for 5 unknown concentration samples was 97.45 ∼ 103.96%. And T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that the SERS combined with characteristic peaks screening methods can be applied to rapidly monitor the chlorpyrifos residue in rice.