ArticleFoods (Basel, Switzerland)2024
The Application of Multi-Parameter Multi-Modal Technology Integrating Biological Sensors and Artificial Intelligence in the Rapid Detection of Food Contaminants.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Next-Generation Bionic Sensors for Small Molecule Detection: Integrating Synthetic Biology, Nanomaterials, and Artificial Intelligence.Micromachines · 2026Review
- Aflatoxin health risk in the feed source: Integrated strategies for feed safety and sustainable One Health.One health (Amsterdam, Netherlands) · 2026Review
- Metal-Organic-Framework-Based Optical Biosensors: Recent Advances in Pathogen Detection and Environmental Monitoring.Sensors (Basel, Switzerland) · 2025Review
- Applications of Artificial Intelligence in Food Industry.Foods (Basel, Switzerland) · 2025Article
- Real-Time Classification of Ochratoxin a Contamination in Grapes Using AI-Enhanced IoT.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.