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The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants

1
Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou 515063, China
2
College of Electronic Engineering, Southwest University, Chongqing 400715, China
3
Hubei Key Laboratory of Food Nutrition and Safety, Huazhong University of Science and Technology, Wuhan 430030, China
*
Author to whom correspondence should be addressed.
Foods 2024, 13(12), 1936; https://doi.org/10.3390/foods13121936
Submission received: 16 May 2024 / Revised: 12 June 2024 / Accepted: 17 June 2024 / Published: 19 June 2024
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Industry)

Abstract

Against the backdrop of continuous socio-economic development, there is a growing concern among people about food quality and safety. Individuals are increasingly realizing the critical importance of healthy eating for bodily health; hence the continuous rise in demand for detecting food pollution. Simultaneously, the rapid expansion of global food trade has made people’s pursuit of high-quality food more urgent. However, traditional methods of food analysis have certain limitations, mainly manifested in the high degree of reliance on personal subjective judgment for assessing food quality. In this context, the emergence of artificial intelligence and biosensors has provided new possibilities for the evaluation of food quality. This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models to highlight the effectiveness and comprehensiveness of artificial intelligence and biosensors in food quality evaluation. The potential prospects and challenges of this method in the field of food safety are comprehensively discussed, aiming to provide valuable references for future research and practice.
Keywords: artificial intelligence; biosensors; feature extraction; machine vision; data analysis artificial intelligence; biosensors; feature extraction; machine vision; data analysis

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MDPI and ACS Style

Zhang, L.; Yang, Q.; Zhu, Z. The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants. Foods 2024, 13, 1936. https://doi.org/10.3390/foods13121936

AMA Style

Zhang L, Yang Q, Zhu Z. The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants. Foods. 2024; 13(12):1936. https://doi.org/10.3390/foods13121936

Chicago/Turabian Style

Zhang, Longlong, Qiuping Yang, and Zhiyuan Zhu. 2024. "The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants" Foods 13, no. 12: 1936. https://doi.org/10.3390/foods13121936

APA Style

Zhang, L., Yang, Q., & Zhu, Z. (2024). The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants. Foods, 13(12), 1936. https://doi.org/10.3390/foods13121936

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