Neurophysiological and multimodal sensory evaluation of baijiu and food pairing liking

Chinese baijiu, a cornerstone of culinary traditions, exhibited diverse flavor profiles that remained poorly understood in food-pairing contexts. This study integrated sensory evaluation, neurophysiological measurements, and machine learning to elucidate pairing liking. Two experiments explored interactions between baijiu types (elegant-flavor vs. chi-flavor) and taste profiles (seven basic tastes/six dishes). Key findings revealed that taste profile compatibility, particularly sweetness and saltiness, emerged as the dominant driver of consumer liking, consistently outperforming sour and bitter combinations. Elegant-flavor baijiu proved particularly well-suited for sweet pairings, whereas chi-flavor baijiu exhibited broader compatibility across taste profiles. Advanced machine learning analysis further revealed that EEG attention levels and facial expressions (Neutral, Sad, and Disgusted) functioned as significant predictors of pairing liking, and that female participants showed reduced liking. Collectively, these findings not only substantiated the sensory similarity theory in the context of baijiu-food pairing but also underscored the potential of integrating neurophysiological data into personalized recommendation systems, thereby advancing both theoretical understanding and practical applications in the food and beverage industry.