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AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring.

Yuliang Zhao¹, Tingting Sun¹, Huawei Zhang¹

¹School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, China.

|September 26, 2025

Summary

Artificial intelligence (AI) enhances electrochemical biosensors for detecting foodborne pathogens like E. coli. AI integration improves sensor design, material optimization, and real-time monitoring for food safety.

Keywords:

artificial intelligence electrochemical biosensors food safety pathogen detection

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Area of Science:

Electrochemistry
Biosensing
Artificial Intelligence

Background:

Electrochemical biosensors are crucial for detecting foodborne pathogens.
Traditional methods face limitations in sensitivity and speed.
Artificial intelligence (AI) offers advanced solutions for biosensing challenges.

Purpose of the Study:

To review the integration of AI, machine learning, and deep learning in electrochemical biosensing for foodborne pathogen detection.
To highlight AI's role in sensor design, material optimization, and signal processing.
To discuss AI-driven advancements for pathogens like E. coli, Salmonella, and S. aureus.

Main Methods:

Systematic review of AI applications in electrochemical biosensor development.
Analysis of AI's impact on recognition molecule design (enzymes, antibodies, aptamers).
Examination of AI in electrochemical parameter tuning and signal analysis.

Main Results:

AI significantly improves sensitivity, enables multiplexed detection, and enhances adaptability in biosensors.
AI integration streamlines sensor design, material selection, and signal interpretation.
Convergence with the Internet of Things (IoT) facilitates portable, real-time detection platforms.

Conclusions:

AI plays a pivotal role across electrochemical biosensor development layers.
Interdisciplinary integration presents opportunities and challenges for practical deployment.
Future research should focus on robust AI models for scalable food safety monitoring.