ArticleCurrent research in food science2025
High-throughput quantification of aflatoxin in moldy peanuts using hyperspectral imaging and CNN: Comparative evaluation of machine learning algorithms and deep learning models.
Article in Current research in food science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Mould deterioration monitoring of Citri Reticulatae Pericarpium using Vis/NIR imaging and an improved Inception ResNet.Frontiers in plant science · 2026Article
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Authors and funding
8 authors.
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Abstract
Aflatoxin contamination in peanuts poses serious health risks, requiring rapid, non-destructive detection methods. This study developed a hyperspectral imaging (HSI) approach combined with deep learning for quantitative aflatoxin analysis in moldy peanuts. The dynamic degradation of nutrients during mold growth was monitored, and correlations between physicochemical properties, aflatoxin levels, and spectral features were investigated. Various preprocessing methods and feature selection techniques were compared, evaluating conventional machine learning (Partial Least Squares Regression (PLSR), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO)) against a convolutional neural network (CNN). The CNN model, optimized with median filtering and genetic algorithm-based feature selection, achieved superior performance (R
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