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 our chemical-tongue platform, a panel of cationic polymers that bear environment-responsive dansyl (Dnc) fluorophores interact nonspecifically yet differentially with the negatively charged bacterial surface, generating unique fluorescence response patterns for each strain. By applying pattern-recognition algorithms, we accurately identified seven Escherichia coli (E. coli) strains. We further demonstrate the practical use of this approach to detect bacterial contamination in milk, which is a pressing public health concern. By combining a brief and effective cell-separation pretreatment with our chemical-tongue platform, we achieve high-precision strain identification and semi-quantitative analysis. Thus, this platform offers a versatile and high-throughput solution for the strain-level analysis of microbial contaminants across a broad range of food products, paving the way for next-generation food-safety management.