Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens

Abstract

How timely identification and determination of pathogen species in pathogen-contaminated foods are responsible for rapid and accurate treatments for food safety accidents. Herein, we synthesize four aggregation-induced emissive nanosilicons with different surface potentials and hydrophobicities by encapsulating four tetraphenylethylene derivatives differing in functional groups. The prepared nanosilicons are utilized as receptors to develop a nanosensor array according to their distinctive interactions with pathogens for the rapid and simultaneous discrimination of pathogens. By coupling with machine-learning algorithms, the proposed nanosensor array achieves high performance in identifying eight pathogens within 1 h with high overall accuracy (93.75-100%). Meanwhile, <i>Cronobacter sakazakii</i> and <i>Listeria monocytogenes</i> are taken as model bacteria for the quantitative evaluation of the developed nanosensor array, which can successfully distinguish the concentration of <i>C. sakazakii</i> and <i>L. monocytogenes</i> at more than 10³ and 10² CFU mL^-1, respectively, and their mixed samples at 10⁵ CFU mL^-1 through the artificial neural network. Moreover, eight pathogens at 1 × 10⁴ CFU mL^-1 in milk can be successfully identified by the developed nanosensor array, indicating its feasibility in monitoring food hazards.