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Biosensors
Volume 15
Issue 9
10.3390/bios15090565
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

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

by
Yuliang Zhao
1,
Tingting Sun
1,
Huawei Zhang
1,
Wenjing Li
1,
Chao Lian
1,
Yongqiang Jiang
2,
Mingyue Qu
3,
Zhongpeng Zhao
2,
Yuhang Wang
2,
Yang Sun
4,
Huiqi Duan
2,
Yuhao Ren
2,
Peng Liu
2,
Xulong Lang
4,* and
Shaolong Chen
2,*
1
School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066000, China
2
State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing 100071, China
3
The PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
4
Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, State Key Laboratory of Pathogen and Biosecurity, Key Laboratory of Jilin Province for Zoonosis Prevention and Control, Changchun 130122, China
*
Authors to whom correspondence should be addressed.
Biosensors 2025, 15(9), 565; https://doi.org/10.3390/bios15090565
Submission received: 18 July 2025 / Revised: 19 August 2025 / Accepted: 26 August 2025 / Published: 28 August 2025
(This article belongs to the Section Biosensor and Bioelectronic Devices)
Versions Notes

Abstract

Artificial intelligence (AI) is transforming electrochemical biosensing systems, offering novel solutions for foodborne pathogen detection. This review examines the integration of AI technologies, particularly machine learning and deep learning algorithms, in enhancing sensor design, material optimization, and signal processing for detecting key pathogens such as Escherichia coli, Salmonella, and Staphylococcus aureus. Key advancements include improved sensitivity, multiplexed detection, and adaptability to complex environments. The application of AI to the design of recognition molecules (e.g., enzymes, antibodies, aptamers), as well as to electrochemical parameter tuning and multicomponent signal analysis, is systematically reviewed. Additionally, the convergence of AI with the Internet of Things (IoT) is discussed as a pathway to portable, real-time detection platforms. The review highlights the pivotal role of AI across multiple layers of biosensor development, emphasizing the opportunities and challenges that arise from interdisciplinary integration and the practical deployment of IoT-enabled technologies in electrochemical sensing systems. Despite significant progress, challenges remain in data quality, model generalization, and interpretability. The review concludes by outlining future research directions for building robust, intelligent biosensing systems capable of supporting scalable food safety monitoring.
Keywords: electrochemical biosensors; artificial intelligence; pathogen detection; food safety electrochemical biosensors; artificial intelligence; pathogen detection; food safety

Share and Cite

MDPI and ACS Style

Zhao, Y.; Sun, T.; Zhang, H.; Li, W.; Lian, C.; Jiang, Y.; Qu, M.; Zhao, Z.; Wang, Y.; Sun, Y.; et al. AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. Biosensors 2025, 15, 565. https://doi.org/10.3390/bios15090565

AMA Style

Zhao Y, Sun T, Zhang H, Li W, Lian C, Jiang Y, Qu M, Zhao Z, Wang Y, Sun Y, et al. AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. Biosensors. 2025; 15(9):565. https://doi.org/10.3390/bios15090565

Chicago/Turabian Style

Zhao, Yuliang, Tingting Sun, Huawei Zhang, Wenjing Li, Chao Lian, Yongqiang Jiang, Mingyue Qu, Zhongpeng Zhao, Yuhang Wang, Yang Sun, and et al. 2025. "AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring" Biosensors 15, no. 9: 565. https://doi.org/10.3390/bios15090565

APA Style

Zhao, Y., Sun, T., Zhang, H., Li, W., Lian, C., Jiang, Y., Qu, M., Zhao, Z., Wang, Y., Sun, Y., Duan, H., Ren, Y., Liu, P., Lang, X., & Chen, S. (2025). AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. Biosensors, 15(9), 565. https://doi.org/10.3390/bios15090565

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