ArticleFoods (Basel, Switzerland)2026
Explainable Artificial Intelligence for Coffee Quality Control: From Coffee Origins to Aroma Intensity.
Article in Foods (Basel, Switzerland), 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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Who cites it
1 citing paper in PubMed.
- Recent Advances in Omics Technology in Food Flavor.Foods (Basel, Switzerland) · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
backgroundCoffee quality is strongly influenced by origin-related factors, or terroir, which shape chemical composition and sensory characteristics. In the specialty coffee sector, where authenticity, traceability, and flavour distinctiveness drive value, understanding the molecular basis of sensory attributes, particularly perceived intensity, is essential.
methodsThis study combined analytical chemistry and explainable artificial intelligence to explore relationships between volatile composition, coffee origin, and sensory intensity. Roasted and ground single-origin coffees from five provenances were analysed using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS). A Support Vector Machine (SVM) classifier discriminated coffee origins based on volatile profile, and SHapley Additive exPlanations (SHAP) identified key compounds. Ridge Regression (RR) was applied to predict sensory intensity values assigned by an expert panel.
resultsThe SVM model classified coffee origins with 91% accuracy, and SHAP analysis highlighted the volatiles most responsible for differentiation. RR predicted sensory intensity with R
conclusionsThis approach connects molecular profile with packaging-declared aroma intensity, offering an indirect yet informative link to sensory perception and illustrating the potential of data-driven methods in sensory science. Overall, the proposed explainable AI approach provides a transparent and reproducible connection between chemical composition, sensory traits, and perceived quality. This strategy supports more objective and traceable quality assessment systems, aligning analytical precision with sensory expertise, which is an essential step toward the evolution of quality control in industrial applications.
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