Investigation of nonlinear relationship of surface enhanced Raman scattering signal for robust prediction of thiabendazole in apple. (1st March 2021)

• Record Type: Journal Article
• Authors: Li, Huanhuan; Mehedi Hassan, Md; Wang, Jingjing; Wei, Wenya; Zou, Min; Ouyang, Qin; Chen, Quansheng
• Is Part Of: Food chemistry. Volume 339(2021)
• Publication Date: 2021-03-01
• Subjects: Thiabendazole -- Runs test -- SERS -- CARS-ELM -- Fuji apples; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2020.127843
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Nonlinear correlation between thiabendazole (TBZ) and SERS data was diagnosed. ACO-ELM, UVE-ELM and CARS-ELM were comparatively investigated for TBZ detection. CARS-ELM model achieved optimum results when principal component was 7. The recoveries of TBZ in spiked apple were ranged from 83.02 to 93.54%. A robust SERS-based method for TBZ was developed with a LOD of 0.001 mg/L. Abstract: 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 × 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.