Exploration of efficient SERS features extraction algorithm for rapid detection of thiabendazole residues in apples.

Long-term exposure to thiabendazole (TBZ) pesticide residues in fruits can have harmful effects on human health. Therefore, this paper proposes a highly sensitive and time-efficient method for the exploration of efficient SERS feature extraction algorithms to establish an optimal quantitative model for the rapid detection of TBZ residues in apples. Specifically, shape-homogeneous Au@Ag nanoparticles (NPs) were developed as label-free substrates, and the optimal detection time of obtaining SERS signals generated by the binding of TBZ to the substrate sample was determined to be 6 min. Recursive Feature Elimination (RFE) and Competitive Adaptive Reweighted Sampling (CARS) were employed for feature extraction algorithms to optimize characteristic peaks and compare them to full spectra variables and extract effective variables on linear (Ridge, PLS) and nonlinear (SVM, RF) models, respectively. Results showed that the RFE-RF (Recursive Feature Elimination-Random Forest) model was more powerful in variable selection and achieved the highest predictive result of TBZ (R P 2 = 0.992, RMSEP = 0.012 μg/mL) and the computed LOD (Limitation of Detection) was 0.1 μg/mL based on PCA (Principal Component Analysis). Comparison with ELISA verified that the designed method is feasible and potentially applicable to guarantee the safety of pesticide residues in fruits. [Display omitted] • The SERS detection time was optimized to be most suitable after the 6th minute. • RFE and CARS were used for feature extraction algorithms to optimize the feature peaks. • Nonlinear algorithms have better predictive power than linear algorithms. • The highest predictions of R2 and RMSEP for RFE-RF were 0.992 and 0.012, respectively. [ABSTRACT FROM AUTHOR]