Fast Detection of Chlorpyrifos Residues in Tea via Surface-Enhanced Raman Spectroscopy Combined with Two-Dimensional Correlation Spectroscopy

In this study, surface-enhanced Raman spectroscopy (SERS) combined with two-dimensional correlation spectroscopy is used to develop a quantitative analysis model for rapidly detecting chlorpyrifos pesticide residues in tea. First, using gold colloid as the enhanced substrate, the spectral data of chlorpyrifos residues in tea samples with different concentrations arc collected via SERS. Then, the original Raman spectra arc pretreated using standard normal variate transformation (SNV). The chlorpyrifos concentration is considered as the disturbance and the characteristic peaks of chlorpyrifos arc screened out via two-dimensional correlation synchronous spectrum and autocorrclation spectrum analysis. Parameters of the support vector machine (SVM) algorithm arc optimized using the gray wolf algorithm (GWO), and the optimized SVM model is used for analyzing the chlorpyrifos residues in tea. The performance of optimized SVM model is compared to that of the model based on partial least squares (PLS). Results show that 14 chlorpyrifos characteristic peaks arc screened using the two-dimensional correlation spectroscopy and the determination coefficient (R-p(2)) of the proposed SVM model in the prediction set is 0.98, the root mean square error of prediction (RMSEP) is 1.32, and the relative prediction deviation (RPD) is 6.32. These values indicate that the developed model can be used for the actual estimation of chlorpyrifos pesticide residues in tea and performs better than the SVM model based on the 1096-cm' feature peak and PLS model. Thus, two-dimensional correlation spectroscopy is suitable for screening characteristic peaks related to chlorpyrifos concentrations in tea. This finding leads to a new idea for optimizing the characteristic variables in Raman spectroscopy. Results also show that SERS combined with two-dimensional correlation spectroscopy can rapidly and accurately detect chlorpyrifos pesticide residues in tea. The proposed method will provide methodological support for the development of rapid detection devices for analyzing pesticide residues in tea.