# Biosensing technologies for foodborne pathogen detection and healthcare: principles, emerging materials, and intelligent platforms

## TL;DR

This review explores advanced biosensing technologies for detecting foodborne pathogens like E. coli and Salmonella, emphasizing smart sensors and AI integration for faster, more accurate field testing.

## Contribution

The paper introduces the integration of AI and ML with biosensors to enable intelligent diagnostics and highlights novel materials and strategies for improving sensor performance.

## Key findings

- Electrochemical, optical, and microfluidic biosensors using nanomaterials and aptamers improve detection accuracy and adaptability in complex food matrices.
- AI and ML enhance biosensors with real-time analytics and multiplexing capabilities, transforming traditional systems into smart diagnostic tools.
- Challenges like matrix interference and bioreceptor instability are addressed through ratiometric sensing and explainable AI strategies.

## Abstract

Foodborne pathogens such as Escherichia coli (E. Coli), Salmonella, and Listeria monocytogenes continue to pose a major potential threat to global public health and therefore rapid, accurate, and field-deployable detection methods are still extremely desirable. This review describes cutting-edge examples of advanced biosensing platforms for the strategy of detecting these priority pathogens, focusing on clinical detection and highlighting electrochemical, optical, and microfluidic sensing modalities. This has been enabled by recent advances in functional nanomaterials, molecular recognition elements (including aptamers and nanozymes), and surface engineering strategies rendering sensors much ‘smarter’/improved in terms of sensitivity, specificity, and behaviour towards complex food matrices. However blending these biosensors with artificial intelligence (AI) and Machine Learning (ML) enabled intelligent pattern recognition, real-time analytics, and multiplexing at high-speed, turning traditional detection systems into smart diagnostic devices. We critically review recent case studies in light of biosensor design, signal transduction mechanisms, models of AI, performance validation, and applicability in different food environments. The principal challenges are identified which include matrix interference, instability of biorecognition elements, limitations in scalability, and the need for regulatory standardization. We discuss these with associated mitigation strategies that are technically sound, including ratiometric sensing, microfluidic pre-treatment techniques, explainable AI, and printable electronics. Forward-looking, we discuss biosensors enabled by being self-powered, biosensor hubs with modular pathogen panels, blockchain incorporation, and standardized validation pipelines. This review offers a prospective view toward enabling intelligent, robust, and regulation-ready biosensing platforms for next-generation food safety monitoring through the bridging of technological innovations with practical implementation.

Foodborne illnesses caused by E. coli, Salmonella, and Listeria monocytogenes remain a global public health concern, driving the demand for rapid, accurate, and field-deployable detection strategies.

This review comprehensively explores advanced biosensing platforms tailored for detecting these priority pathogens, highlighting progress in electrochemical, optical, and microfluidic sensing mechanisms.

Integrating functional nanomaterials, molecular recognition elements such as aptamers and nanozymes, and surface engineering techniques has significantly enhanced sensor sensitivity, specificity, and adaptability to complex food matrices.

Moreover, the convergence of biosensors with AI and ML has enabled intelligent pattern recognition, real-time analytics, and high-throughput multiplexing.

Key challenges, including matrix interference, bioreceptor instability, manufacturing scalability, and regulatory standardization, are discussed.