Artificial neural network-facilitated V2C MNs-based colorimetric/fluorescence dual-channel biosensor for highly sensitive detection of AFB1 in peanut

Abstract

In this work, V2C Mxene nano-enzyme materials (V2C MNs) with excellent peroxidase-like activity and fluorescence quenching performance were prepared, and it was modified using 6-carboxyfluorescein-labelled aptamers (ssDNA-FAM) to construct a novel dual-mode sensor V2C@ssDNA-FAM, with detection limits of 0.0477 ng mL−1 and 0.2789 ng mL−1 of fluorescence (linear range of 0.1–550 ng mL−1) and colorimetric (linear range of 1–1000 ng mL−1) modes, respectively. Meanwhile, an ANN intelligent detection platform has been constructed, which could automatically track and analyze the fluorescence and colorimetric signal of the detection system through machine learning and immediately obtain the AFB1 concentration, and the detection limits of the fluorescence (linear range of 0.1–500 ng mL−1) and colorimetric (linear range of 1–800 ng mL−1) channels of it were 0.0905 ng mL−1 and 0.6845 ng mL−1, respectively. The recovery rates of fluorescence, colorimetric sensing detection and ANN-assisted fluorescence and colorimetric sensing detection of real samples ranged from 95.40% to 101.76%. The method constructed in this work was superior to most existing literature reports and had great potential for application in the field of food quality testing.