Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP

Abstract

Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The quenching of unincorporated amplification signal reporters (QUASR) was first introduced in multiplex digital LAMP. During amplification, primers labeled with different fluorophores were integrated into amplicons, generating color-specific fluorescent spots. While excess primers were quenched by complementary quenching probes. After amplification, fluorescent spots in red, green, and blue emerged in hydrogels, which were automatically identified and quantified using a deep learning model. Methicillin-resistant Staphylococcus aureus and carbapenem-resistant Escherichia coli in real fruit and vegetable samples were also successfully detected. This artificial intelligence-driven color-encoded multiplex hydrogel LAMP offers promising potential for the digital quantification of antibiotic-resistant bacteria in the food industry.

Keywords: Antibiotic-resistant bacteria; Deep learning; Digital LAMP; Food safety; Multiplex detection.