LAUSR

Abstract Electrochemical nanozyme sensor coupled with machine learning (ML) for intelligent analysis of carbendazim (CBZ) residues in tea and rice samples using graphene-like molybdenum disulfide (MoS2)/multi-walled carbon nanotubes (MWCNTs) porous nanohybrid network with oxidase-like characteristic was developed. MoS2/MWCNTs porous nanohybrid network was prepared by ultrasonic dispersion of MWCNTs into MoS2 aqueous dispersion that was obtained by ultrasonic-assisted dispersion in sodium carboxymethyl cellulose water solution, which displayed low electron transfer resistance, high effective surface area, excellent film electrode stability. Machine learning via the artificial neural network for the intelligent analysis of CBZ was discussed in comparison with traditional regression analysis. Good electrocatalytic capacity, unique oxidase-like characteristic, wide linear range with 0.04–100 μM, low limit of detection with 7.4 nM, high sensitivity, and satisfactory practicability for CBZ demonstrated that electrochemical nanozyme sensor for the intelligent analysis of CBZ residues in edible agro-products was feasible. This will provide a new electrochemical sensing platform for the development of a graphene-like nanozyme sensor and its intelligent analysis of CBZ in food and agro-product safety.