ReviewToxins2025
Detection of Mycotoxins in Cereal Grains and Nuts Using Machine Learning Integrated Hyperspectral Imaging: A Review.
Review in Toxins, 2025. 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
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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.
- Review
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- 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
- Leveraging artificial intelligence for mycotoxin management in food systems.NPJ science of food · 2026Review
- Feasibility of Hyperspectral Imaging and Machine Learning for Rapid Prescreening of Aflatoxin BToxins · 2026Article
- Article
- Weakly Supervised Fine-Grained Discrimination of Wheat Mold Using Local RGB-HSI Fusion.Foods (Basel, Switzerland) · 2026Article
- Aflatoxin health risk in the feed source: Integrated strategies for feed safety and sustainable One Health.One health (Amsterdam, Netherlands) · 2026Review
- Close-Range 3D Hyperspectral Measurement System with a Physics-Guided Spectral Correction Model.Sensors (Basel, Switzerland) · 2026Article
- Rice Quality: A Multidimensional Evaluation Integrating Ecology, Management and Genetic Regulation.Foods (Basel, Switzerland) · 2026Review
- Article
- VNIR Hyperspectral Signatures for Early Detection and Machine-Learning Classification of Wheat Diseases.Plants (Basel, Switzerland) · 2025Article
- Silent Saboteurs: Decoding Mycotoxins-From Chemistry and Prevalence to Health Risks, Detection, Management and Emerging Frontiers.Journal of fungi (Basel, Switzerland) · 2025Review
- Emerging nano sensing technologies for the detection of biological and chemical food toxins.RSC advances · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cereal grains and nuts are the world's most produced food and the economic backbone of many countries. Food safety in these commodities is crucial, as they are highly susceptible to mold growth and mycotoxin contamination in warm, humid environments. This review explores hyperspectral imaging (HSI) integrated with machine learning (ML) algorithms as a promising approach for detecting and quantifying mycotoxins in cereal grains and nuts. This study aims to (1) critically evaluate current non-destructive techniques for processing these foods and the applications of ML in identifying mycotoxins through HSI, and (2) highlight challenges and potential future research directions to enhance the reliability and efficiency of these detection systems. The ML algorithms showed effectiveness in classifying and quantifying mycotoxins in grains and nuts, with HSI systems increasingly adopted in industrial settings. Mycotoxins exhibit heightened sensitivity to specific spectral bands within HSI, facilitating accurate detection. Additionally, selecting only relevant spectral features reduces ML model complexity and enhances reliability in the detection process. This review contributes to a deeper understanding of the integration of HSI and ML for food safety applications in cereal grains and nuts. By identifying current challenges and future research directions, it provides valuable insights for advancing non-destructive mycotoxin detection methods in the food industry using HSI.
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Registered trials
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