Rapid detection and strain-level identification of milk-borne bacteria using a polymer-based chemical tongue

The detection and strain-level identification of bacteria in food are critical for public health; however, conventional methods typically require expensive equipment, lengthy protocols, and/or specialized expertise. Here, we report a 'chemical tongue' strategy, i.e., an analytical approach inspired by the human gustatory system, for the rapid and user-friendly strain-level sensing of foodborne bacteria. In this platform, a panel of cationic polymers that bear environment-responsive dansyl fluorophores interact nonspecifically yet differentially with the negatively charged bacterial surface, generating strain-specific fluorescence response patterns. By applying pattern-recognition algorithms, we accurately discriminated seven Escherichia coli (E. coli) strains. We further demonstrate the practical applicability of this approach to bacterial analysis in milk by integrating a brief and effective pretreatment that suppresses matrix-derived interference. This enables reliable strain-level identification across different milk matrices, discrimination of E. coli strains in the presence of spoilage-associated psychrotrophic bacteria, and time-dependent monitoring of milk quality changes induced by bacterial growth. Taken together, our chemical-tongue platform provides a rapid and cultivation-free solution for strain-level analysis of microbial contamination in complex food matrices, offering a promising foundation for next-generation food quality monitoring and safety management.