Strain-Level Food Surveillance of Escherichia coli Using a Specific-Nonspecific Hybrid Sensor Array Strategy.

Area of Science:

• Analytical Chemistry
• Biosensing
• Food Science

Background:

• Array-based sensing offers high-throughput detection but struggles with selectivity in complex food matrices.
• Accurate detection of pathogenic *Escherichia coli* (E. coli) is crucial for food safety.
• Existing methods often lack the necessary precision for real-world food analysis.

Purpose of the Study:

• To develop a high-performance sensor array for rapid and accurate analysis of *Escherichia coli* (E. coli).
• To integrate specific and cross-reactive recognition for enhanced selectivity and strain differentiation.
• To address the challenge of detecting pathogenic bacteria in complex food environments.

Main Methods:

• Utilized a genus-specific aptamer for selective capture and pre-enrichment of *E. coli*.
• Employed a multivalent antibody targeting *E. coli* O157:H7 for strain-level differentiation.
• Incorporated boronate chemistry to amplify interstrain differences based on surface carbohydrates.
• Applied K-nearest neighbors (KNN) machine learning for data analysis and classification.

Main Results:

• The sensor array generated multidimensional response fingerprints for simultaneous identification of *E. coli* strains and mixtures within 1 hour.
• Achieved accurate bulk quantification and robust discrimination in buffer and complex food matrices (beef, milk).
• The KNN model demonstrated optimal performance, achieving 100% classification accuracy for *E. coli* detection.

Conclusions:

• The synergistic sensing strategy provides a versatile and translatable platform for detecting foodborne pathogenic *E. coli*.
• This approach overcomes limitations of selectivity in complex matrices, crucial for real-world food safety monitoring.
• The developed sensor array enables rapid, precise, and accurate identification of *E. coli* strains.