A sensitive silver nanoflower-based SERS sensor coupled novel chemometric models for simultaneous detection of chlorpyrifos and carbendazim in food

In this study, a simultaneous detection platform utilizing SERS coupled chemometric models was developed to sensitively detect chlorpyrifos and carbendazim pesticides in rice in a single step for the first time. Initially, silver nanoflowers (AgNFs) were synthesized and used as the SERS substrate to acquire the spectra of pesticides. AgNFs have multiple chinks on their surface that can generate strong electromagnetic enhancement for SERS signal of chlorpyrifos and carbendazim. To obtain effective variables from the dataset, a novel wavelength selection method called interval combination population analysis (ICPA) was used. The ICPA-partially least square (ICPA-PLS) method showed a significant linear relationship over the concentration range of 0.01–1000 μg/mL for both pesticides when compared to other established chemometric methods. This model, yielded correlation coefficients in the calibration of 0.9802 and 0.9995, with an RPD of 3.74 and 7.8 for chlorpyrifos and carbendazim, respectively. A limit of detection of 0.01 μg/mL was determined using PCA analysis. Finally, the method was compared to HPLC, and the results revealed no significant differences (t < t0.05 (3), P>0.05) between two methods, implying that the proposed method could be used for food safety monitoring.