Strain-Level Food Surveillance of <i>Escherichia coli</i> Using a Specific-Nonspecific Hybrid Sensor Array Strategy

Array-based sensing platforms have advanced rapidly in recent years, offering strong potential for high-throughout and multiplexed detection. However, their practical translation in food safety remains constrained by insufficient selectivity in complex matrices. Here, we report a synergistic sensing strategy that integrates specific and cross-reactive recognition to construct a high-performance sensor array for rapid and accurate Escherichia coli analysis. A genus-specific aptamer is employed as a front-end module to selectively capture and pre-enrich E. coli, enabling bulk concentration quantification and effective suppression of non- E. coli interference prior to array-based discrimination. Strain-level differentiation is subsequently achieved by exploiting the cross-reactivity of a multivalent antibody targeting E. coli O157:H7 across related strains. To further amplify interstrain differences, boronate chemistry is incorporated through 4-mercaptophenylboronic acid and cysteamine, providing differential binding to cis–diol motifs on bacterial surface carbohydrates. The resulting sensor array generates multidimensional response fingerprints and enables the simultaneous identification of representative E. coli strains and complex mixtures within 1 h. Accurate bulk quantification and robust discrimination are demonstrated across a wide concentration range in buffer and in complex food matrices, including beef and milk. By benchmarking four machine-learning algorithms, the K-nearest neighbors (KNN) model delivered optimal performance, achieving 100% classification accuracy. Collectively, this work establishes a versatile and translatable sensing platform for rapid, precise detection of foodborne pathogenic E. coli, addressing a critical challenge in real-world food safety monitoring.