Qualitative and quantitative analysis of four benzimidazole residues in food by surface-enhanced Raman spectroscopy combined with chemometrics

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were devel-oped for qualitative and quantitative analysis of four benzimidazole (BMZs) residues in corn. Sulfhydryl func-tionalized Fe3O4@SiO2@Ag-SH magnetic SERS substrates were prepared to obtain the SERS spectra of four BMZs for chemometric analysis. The partial least squares regression discrimination analysis (PLS-DA) model performed best, with a recall rate upwards 99.17%, and could successfully distinguish four BMZs. Under the support vector machine regression (SVR) model, the detection limits of carbendazim, benomyl, thiophanate-methyl and thia-bendazole were 0.055 mg/L, 0.056 mg/L, 0.067 mg/L and 0.093 mg/L, respectively; the average recovery was in the range of 85.6%-107.5%. Furthermore, the method verified by HPLC, and the results showed that there was no significant difference between two methods (p > 0.05). Therefore, the strategy based on SERS coupling chemometrics can be served as a promising tool for rapid determination of BMZs residues in food.

Automated summary

In this study, surface-enhanced Raman spectroscopy (SERS) combined with chemometric methods were used to analyze four benzimidazole (BMZs) residues in corn. Sulfhydryl functionalized Fe3O4@SiO2@Ag-SH magnetic SERS substrates were prepared for the spectral analysis and chemometric analysis was performed using the partial least squares regression discrimination analysis (PLS-DA) model. The results showed that this strategy could rapidly determine BMZs residues in food with high accuracy and no significant difference compared to HPLC.