ArticleFood chemistry: X2026
Machine learning assisted intelligent identification strategy for adulteration peanut oil based on fatty acid GC fingerprint.
Article in Food chemistry: X, 2026. 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.
- AI-Driven Food Fraud Detection Systems: A Critical Systematic Review of the Detection-Prevention Gap.Foods (Basel, Switzerland) · 2026Review
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11 authors.
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Abstract
This study established an intelligent identification method for adulterated peanut oil using machine learning. A total of 32 pure peanut oil samples and 126 adulterated samples containing 5%-30% soybean, palm, cottonseed, or sunflower oil were prepared. The fatty acid profiles of pure and adulterated oils were highly similar, making them difficult to distinguish effectively using principal component analysis or heatmaps. By applying four supervised machine learning algorithms (support vector machine (SVM), random forest, partial least squares, and decision tree), the classification accuracy is improved significantly. The SVM model performed best, achieving 98.18%-100% accuracy for both single and mixed adulteration samples. SHAP analysis identified lignoceric acid (C24:0) as the key adulteration marker. The regression model yielded R
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