SynthesisToxins2024
Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review.
Synthesis in Toxins, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The use of artificial intelligence to improve mycotoxin management: a review.Mycotoxin research · 2025Pooled it
- Pre-Harvest Aflatoxin Contamination in Crops and Climate Change Factors: A European Overview.Toxins · 2025Pooled it
- Review
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- Review
- A Preliminary Food-AI Readiness Framework for AI-Enabled Hyperspectral Systems in Fungal and Mycotoxin Risk Management: A Critical Narrative Review.Foods (Basel, Switzerland) · 2026Review
- Identification and Detection of AflatoxigenicToxins · 2026Review
- Leveraging artificial intelligence for mycotoxin management in food systems.NPJ science of food · 2026Review
- Artificial Intelligence in Food Safety: A Tertiary Study.Comprehensive reviews in food science and food safety · 2026Review
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- Cancer and Environmental Xenobiotics: Mechanisms, Controversies, and Innovations.Journal of xenobiotics · 2025Review
- Silent Saboteurs: Decoding Mycotoxins-From Chemistry and Prevalence to Health Risks, Detection, Management and Emerging Frontiers.Journal of fungi (Basel, Switzerland) · 2025Review
- Holistic concept guided quality control of traditional Chinese medicines for optimizing bioavailability.Chinese medicine · 2025Review
- Mycotoxins in food and agriculture: from occurrence to economic consequences.Archives of microbiology · 2025Review
- Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning.Food chemistry: X · 2025Review
- Aflatoxin Contamination in Agri-Food Systems: A Comprehensive Review of Toxicity, Food Security, Economic Impacts, and Sustainable Mitigation Across the Value Chain.Food science & nutrition · 2025Review
- Review
- Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms.Food chemistry: X · 2025Article
- Mycotoxins in Food: Cancer Risks and Strategies for Control.Foods (Basel, Switzerland) · 2024Review
- Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.Foods (Basel, Switzerland) · 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
4 authors.
Funding
Abstract
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.
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.