ArticleFrontiers in human neuroscience2025
Tuning into flavor: predicting coffee sensory attributes from EEG with boosted-tree regression models.
Article in Frontiers in human neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Odor-induced sweetness enhancement: EEG evidence for olfactory and gustatory cross-modal interactions.Current research in food science · 2026Article
- Integrating Cutting-Edge Technologies in Food Sensory and Consumer Science: Applications and Future Directions.Foods (Basel, Switzerland) · 2025Review
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Authors and funding
5 authors.
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Abstract
Flavor, a multimodal perception based on taste, smell, and tactile cues, plays a significant role in consumer preferences and purchase intentions toward coffee. In this exploratory study, we assessed the potential of electroencephalography (EEG) and machine learning (ML) techniques to predict coffee sensory attributes. We extracted spectral and temporal features from a professional panel while tasting coffee samples and basic water solutions. We trained multiple Least-Squares Boosted Trees (LSBoost) and optimized their hyperparameters through a 100-step Bayesian approach based on a Leave-One-Subject-Out (LOSO) scheme. The models achieved, overall, high predictive accuracy (MAE < 0.75 on a 0 - 10 scale) and medium-to-large robustness (Cohen's
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