ReviewCurrent research in food science2024
Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection.
Review in Current research in food science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Recent insights for fish allergens: novel processing techniques and detection technology.Journal of the science of food and agriculture · 2026Review
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- AI-Assisted Electrochemical Immunosensing for Matrix-Aware Detection of Aflatoxin M1 and Atrazine in Food Matrices.Biosensors · 2026Article
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- Recent Advances in Electrochemical Biosensors for the Detection of Milk Adulterants.Biosensors · 2026Review
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Application of Machine Learning in Food Safety Risk Assessment.Foods (Basel, Switzerland) · 2025Review
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- TiOMicromachines · 2025Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- Nanostructure-Engineered Optical and Electrochemical Biosensing Toward Food Safety Assurance.Foods (Basel, Switzerland) · 2025Review
- A deep learning model with machine vision system for recognizing type of the food during the food consumption.Scientific reports · 2025Article
- Advancing microbial risk assessment: perspectives from the evolution of detection technologies.NPJ science of food · 2025Review
- AI-Powered Innovations in Food Safety from Farm to Fork.Foods (Basel, Switzerland) · 2025Review
- Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.Frontiers in veterinary science · 2025Article
- Sensing the Future-Frontiers in Biosensors: Exploring Classifications, Principles, and Recent Advances.ACS omega · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.
Indexed as
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What OpenQuestion holds
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.