A novel multimodal nano-sensor detection system based on artificial intelligence and two-dimensional Mxenes for Ochratoxin A in food

Abstract

Ochratoxin A (OTA), which is prone to contaminating food products, poses a significant threat to human health. To accurately detect and provide timely early warnings for OTA contamination (COTA) in food, a novel four-modal nano-biosensor detection system based on MXenes was developed in this work. MXenes is a new family of two-dimensional materials including vanadium carbide (V₂C) MXenes, which has great potential in the field of biosensing and detection due to its large specific surface area, rich surface functional groups, and good chemical stability. By in-situ chemical etching, metal ion intercalation, and synergistic physical exfoliation techniques, V₂C nano-materials (V₂C-NMS) with excellent fluorescence quenching characteristics were synthesized, and V₂C-NMS@ssDNA with excellent peroxidase like activity was constructed by functionalizing V₂C-NMS with adapter. Based on this, a fluorescence/colorimetric biosensor for OTA was constructed, and the fluorescence/colorimetric signal output by the sensor had good linear relationship with COTA, with the detection limit (LOD) as low as 6.77 pg mL⁻¹. Furthermore, a fully connected artificial neural network (FCANN) was developed based on a series of RGB values obtained from the fluorescence/colorimetric mode of the biosensor, and the fluorescence/colorimetry channel of the FCANN could accurately predict the COTA of the sample on-site or remotely in just a few seconds, with the LOD as low as 7.10 pg mL⁻¹. Importantly, the four-mode method performed well in real sample detection, with recovery rates ranging from 95.33% to 105.79%, and the detection results of the four modalities could be mutually verified.