Enterprise AI Analysis
The use of artificial intelligence to improve mycotoxin management: a review
The management of mycotoxin contamination in the supply chain is continuously evolving in response to growing knowledge about mycotoxins, shifting factors that influence mycotoxin occurrence, and ongoing technological developments. One of the technological developments is the potential for using artificial intelligence (AI) in mycotoxin management. AI can be used in various fields of mycotoxin management, including for predictive modelling of mycotoxins and for analytical detection and analyses. This review aimed to investigate the state-of-the-art of the use of AI for mycotoxin management. This review focuses on (1) predictive models for the presence of mycotoxins in commodities at both pre-harvest and post-harvest levels and (2) the detection of mycotoxins in samples by processing large datasets resulting from imaging data or chemical analyses of the sample. A systematic review was conducted, resulting in a total of 70 relevant references, including 15 references focusing on mycotoxin prediction models and 54 references focusing on mycotoxin detection, ranging from imaging to chemical analysis, and including relevant reviews. The AI applications and the most popular AI algorithms are presented. As shown by this review, AI is able to improve mycotoxin prediction models both at pre- and post-harvest levels and makes the emergence of non-invasive and fast detection methods such as imaging detection or electronic noses possible. A major challenge remains in the applicability and scalability of AI models to practical settings.
Key AI-Driven Improvements
AI is rapidly transforming mycotoxin management. Here’s a snapshot of its current impact and potential:
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AI Prediction Models
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AI Detection Models
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Highest R² for Prediction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Pre-harvest Prediction
Post-harvest & In Vitro Prediction
Spectral Imaging Detection
Non-imaging Detection
Mycotoxin prediction at the pre-harvest level is crucial for early intervention. AI models, often combined with mechanistic approaches, leverage diverse data to forecast contamination risks with improved accuracy.
AI-Enhanced Pre-harvest Risk Assessment
Mechanistic models were combined with AI algorithms to estimate mycotoxin risk index based on weather variables, crop system data, and kernel moisture, achieving high accuracy.
Mechanistic Model (Weather)
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Risk Index Calculation
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AI Model Integration (Crop Data)
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Mycotoxin Class Prediction
| Feature | Mechanistic Models | AI Models |
|---|---|---|
| Predictive Performance (DON) | 84% (Liu et al. 2018) | 90% (Liu et al. 2018) |
| Predictive Performance (Aflatoxins/Fumonisins) | 53% (Camardo Leggieri et al. 2021a, b) | 79% (Camardo Leggieri et al. 2021a, b) |
| Input Data Complexity | Limited variables | Large, diverse datasets |
AI also plays a critical role in predicting mycotoxin development post-harvest and under controlled in vitro conditions, aiding in optimal storage and processing strategies.
High R² in Fungal Growth Prediction
Srinivasan et al. (2022) used neural network, regression tree, and random forest to model Fusarium culmorum growth and mycotoxin production in milled maize, achieving an R² of 0.95.
0 Highest R² for Fungal Growth PredictionAI-Driven Quality Control in Grain Storage
Problem: Predicting mycotoxin contamination (DON/Aflatoxins) in stored wheat batches before visual spoilage or widespread impact.
Solution: A neural network was trained using CO2 respiration rates and early visual cues of mould formation to forecast contamination.
Outcome: Achieved an 83% prediction accuracy, enabling proactive intervention and reducing spoilage.
Non-invasive spectral imaging techniques, combined with AI, offer rapid and efficient detection of mycotoxins in bulk samples, enabling quick decision-making in the supply chain.
Non-Invasive Mycotoxin Screening Workflow
Non-invasive detection techniques such as spectral imaging (fluorescence, near-infrared, hyperspectral) are used to screen bulk samples. AI processes spectral data and performs band selection to classify samples into contaminated classes. Accuracies often exceed 90%.
Sample Collection
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Spectral Imaging (NIR, HSI, Fluorescence)
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AI Data Processing & Feature Extraction
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Mycotoxin Contamination Classification
Peak Accuracy in Hyperspectral Imaging
Best performing AI algorithms combined with hyperspectral imaging achieved accuracies well above 90% for aflatoxin, fumonisins, and deoxynivalenol detection in various commodities.
0% Max Accuracy with Hyperspectral ImagingBeyond imaging, other non-invasive methods like electronic noses and biosensors, powered by AI, are emerging for rapid and precise mycotoxin detection.
| Method | Key Principle | AI Algorithms | Performance Example |
|---|---|---|---|
| Electronic Nose (E-nose) | Volatile Organic Compound Fingerprinting | Classification Tree, ANN | 83% accuracy for DON in wheat |
| Surface-Enhanced Raman Spectroscopy (SERS) | Raman Signal Interpretation | Convolutional Neural Networks | R² > 0.98, 98.8% accuracy for DON |
AI-Enhanced Biosensors for Rapid Mycotoxin Identification
Problem: Need for fast, accurate, and non-invasive methods to detect mycotoxins in food and feed samples on-site.
Solution: Biosensors capable of recognizing specific mycotoxin elements are coupled with AI algorithms for data interpretation.
Outcome: Enables rapid, high-accuracy detection and classification of mycotoxins, facilitating quicker decision-making in food safety management.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve with advanced AI mycotoxin management.
Annual Savings Estimate
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Your AI Implementation Roadmap
A strategic overview of how AI can be integrated into your mycotoxin management, from data to deployment.
Data Collection & Integration
Establish protocols for collecting diverse, high-quality environmental, agronomic, spectral, and sensor data; integrate existing data sources.
Model Development & Training
Develop and train AI algorithms (NN, SVM, RF) using integrated datasets for predictive modeling and detection tasks.
Validation & Calibration
Conduct rigorous internal and external validation using independent datasets to ensure model robustness and generalizability across real-world conditions.
Deployment & Integration
Integrate validated AI models into operational dashboards, on-site detection devices, or in-line systems within the supply chain.
Continuous Monitoring & Improvement
Implement continuous data feedback loops for model retraining, performance monitoring, and iterative improvement.
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