ArticleCurrent research in food science2023
Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy system with machine learning.
Article in Current research in food science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Flow-Through Aptamer-SERS Microfluidic Platform for Aflatoxin B1 Detection in Food Crops.ACS omega · 2026Article
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning.Food chemistry: X · 2025Review
- Current Approaches to Aflatoxin B1 Control in Food and Feed Safety: Detection, Inhibition, and Mitigation.International journal of molecular sciences · 2025Review
- Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples.Scientific reports · 2025Article
- Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions.Current research in food science · 2025Article
- Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review.Foods (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Consumption of aflatoxin-contaminated food can cause severe illness when consumed by humans or livestock. Because the mycotoxin frequently occurs in cereal grains and other agricultural crops, it is crucial to develop portable devices that can be used non-destructively and in real-time to identify aflatoxin-contaminated food materials during early stages of harvesting or processing. In this study, an aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models evaluated, such as linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines and spectral preprocessing methods, the best classification accuracy was achieved at 95.7% using LDA in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Partial least squares regression (PLSR) models demonstrated a close-range accuracy within the scope of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R
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