ReviewFoods (Basel, Switzerland)2024
Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.
Review 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 16 papers, 2 of them syntheses that pooled it.
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, 2 syntheses or guidelines pooled it.
- Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.Mycotoxin research · 2026Pooled it
- The use of artificial intelligence to improve mycotoxin management: a review.Mycotoxin research · 2025Pooled it
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- Leveraging artificial intelligence for mycotoxin management in food systems.NPJ science of food · 2026Review
- Emerging Chemical Hazards in Animal-Derived Foods: From Public Health to Detection and Control Solutions.China CDC weekly · 2026Article
- Biological Detoxification of Mycotoxins by Lactic Acid Bacteria: Safeguarding Food from Fungal Contaminants.Toxins · 2026Review
- Impact ofToxins · 2026Article
- Artificial Intelligence in Food Safety: A Tertiary Study.Comprehensive reviews in food science and food safety · 2026Review
- Mycotoxins and plant diseases in a changing climate: from pathogen ecology to smart surveillance and mitigation strategies.Frontiers in fungal biology · 2026Review
- Smart Probiotic Solutions for Mycotoxin Mitigation: Innovations in Food Safety and Sustainable Agriculture.Probiotics and antimicrobial proteins · 2026Review
- Silent Saboteurs: Decoding Mycotoxins-From Chemistry and Prevalence to Health Risks, Detection, Management and Emerging Frontiers.Journal of fungi (Basel, Switzerland) · 2025Review
- Review
- Review
- Nanozyme-Powered Multimodal Sensing for Pesticide Detection.Foods (Basel, Switzerland) · 2025Review
- New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods.Toxins · 2025Review
- Automation and Optimization of Food Process Using CNN and Six-Axis Robotic Arm.Foods (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.
Indexed as
Identifiers
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.