Investigation of nonlinear relationship of surface enhanced Raman scattering signal for robust prediction of thiabendazole in apple

Thiabendazole (TBZ) is extensively used in agriculture to control molds; residue of TBZ may pose a threat to humans. Herein, surface-enhanced Raman spectroscopy (SERS) coupled variable selected regression methods have been proposed as simple and rapid TBZ quantification technique. The nonlinear correlation between the TBZ and SERS data was first diagnosed by augmented partial residual plots method and calculated by runs test. Au@Ag NPs with strong enhancement factor (EF = 4.07 x 10(6)) of Raman signal was used as SERS active material to collect spectra from TBZ. Subsequently, three nonlinear regression models were comparatively investigated and the competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) achieved a higher correlation coefficient (Rp(2) = 0.9406) and the lower root-mean-square-error of prediction (RMSEP = 0.5233 mg/L). Finally, recoveries of TBZ in apple samples were 83.02-93.54% with relative standard deviation (RSD) value < 10%. Therefore, SERS coupled CARS-ELM could be employed as a rapid and sensitive approach for TBZ detection in Fuji apples.

Automated summary

Thiabendazole (TBZ) is commonly used in agriculture to control molds, but its residues may pose a threat to humans. This study suggests the use of SERS coupled with variable selected regression methods for rapid and sensitive quantification of TBZ. The CARS-ELM model shows high correlation coefficient and low prediction error, making it a promising approach for TBZ detection in Fuji apples.