Academic Journal
AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring.
| Title: | AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. |
|---|---|
| Authors: | Zhao, Yuliang, Sun, Tingting, Zhang, Huawei, Li, Wenjing, Lian, Chao, Jiang, Yongqiang, Qu, Mingyue, Zhao, Zhongpeng, Wang, Yuhang, Sun, Yang, Duan, Huiqi, Ren, Yuhao, Liu, Peng, Lang, Xulong, Chen, Shaolong |
| Source: | Biosensors (2079-6374); Sep2025, Vol. 15 Issue 9, p565, 27p |
| Subject Terms: | Artificial intelligence, Electrochemical sensors, Deep learning, Internet of things, Machine learning, Food pathogens, Biosensors, Food safety |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Biosensors (2079-6374) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Yuliang%22">Zhao, Yuliang</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Tingting%22">Sun, Tingting</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huawei%22">Zhang, Huawei</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Wenjing%22">Li, Wenjing</searchLink><br /><searchLink fieldCode="AR" term="%22Lian%2C+Chao%22">Lian, Chao</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yongqiang%22">Jiang, Yongqiang</searchLink><br /><searchLink fieldCode="AR" term="%22Qu%2C+Mingyue%22">Qu, Mingyue</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zhongpeng%22">Zhao, Zhongpeng</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuhang%22">Wang, Yuhang</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Yang%22">Sun, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Duan%2C+Huiqi%22">Duan, Huiqi</searchLink><br /><searchLink fieldCode="AR" term="%22Ren%2C+Yuhao%22">Ren, Yuhao</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Peng%22">Liu, Peng</searchLink><br /><searchLink fieldCode="AR" term="%22Lang%2C+Xulong%22">Lang, Xulong</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shaolong%22">Chen, Shaolong</searchLink> – Name: TitleSource Label: Source Group: Src Data: Biosensors (2079-6374); Sep2025, Vol. 15 Issue 9, p565, 27p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Electrochemical+sensors%22">Electrochemical sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Food+pathogens%22">Food pathogens</searchLink><br /><searchLink fieldCode="DE" term="%22Biosensors%22">Biosensors</searchLink><br /><searchLink fieldCode="DE" term="%22Food+safety%22">Food safety</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Biosensors (2079-6374) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/bios15090565 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 565 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Electrochemical sensors Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Food pathogens Type: general – SubjectFull: Biosensors Type: general – SubjectFull: Food safety Type: general Titles: – TitleFull: AI-Enhanced Electrochemical Sensing Systems: A Paradigm Shift for Intelligent Food Safety Monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Yuliang – PersonEntity: Name: NameFull: Sun, Tingting – PersonEntity: Name: NameFull: Zhang, Huawei – PersonEntity: Name: NameFull: Li, Wenjing – PersonEntity: Name: NameFull: Lian, Chao – PersonEntity: Name: NameFull: Jiang, Yongqiang – PersonEntity: Name: NameFull: Qu, Mingyue – PersonEntity: Name: NameFull: Zhao, Zhongpeng – PersonEntity: Name: NameFull: Wang, Yuhang – PersonEntity: Name: NameFull: Sun, Yang – PersonEntity: Name: NameFull: Duan, Huiqi – PersonEntity: Name: NameFull: Ren, Yuhao – PersonEntity: Name: NameFull: Liu, Peng – PersonEntity: Name: NameFull: Lang, Xulong – PersonEntity: Name: NameFull: Chen, Shaolong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20796374 Numbering: – Type: volume Value: 15 – Type: issue Value: 9 Titles: – TitleFull: Biosensors (2079-6374) Type: main |
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