A novel four-modal nano-sensor based on two-dimensional Mxenes and fully connected artificial neural networks for the highly sensitive and rapid detection of ochratoxin A

Abstract

Timely and accurate on-site detection of ochratoxin A (OTA) is extremely important for global public health. In this study, a fluorescence/colorimetric biosensor based on Ti3C2 nano-materials (Ti3C2-NMS) and a machine-learning (ML) based fluorescence/colorimetric intelligent learning system for detection of OTA concentration (COTA) were developed. The sensor was fabricated by functionalizing Ti3C2-NMS prepared by physical-exfoliation assisted metal-ion-induction using ssDNA. The Ti3C2-NMS exhibited good fluorescence quenching characteristics (FQC) and peroxidase-like activity (PLA). More surprisingly, the functionalization of Ti3C2-NMS by ssDNA further enhanced the FQC and PLA of the material, which could be used for dual-mode detection of OTA. When different COTA existed, ssDNA competitively bound to OTA, resulting in regular changes in fluorescence and colorimetric signals of the sensor, which realized the accurate and sensitive biosensing detection of OTA in two modalities. Based on a series of fluorescent/colorimetric RGB datasets collected by a self-developed application, a dual-channel ML model had been developed. This model can be integrated into mobile phones, clouds, and PCs to achieve intelligent sensing detection of OTA with the assistance of fully connected artificial neural networks. The method constructed had high specificity, low cost, and fast responsiveness, with a LOD as low as 1.58 pg mL−1, indicating excellent potential for application and promotion.