AI-driven prediction of consumer liking of coffee from sensory data

Abstract Understanding and predicting consumer acceptance is critical to commercial success in the coffee industry. This study presents a robust data analysis framework to deconstruct consumer preference using a dataset where 118 consumers rated their liking of 27 black drip coffee samples, the adequacy of select attributes on just-about-right (JAR) scales, and the sensory profile of the coffees with a check-all-that-apply (CATA) task. We integrated four feature-ranking methods to identify key sensory drivers, which informed the development of predictive models to forecast consumer liking. A novel consumer segmentation technique was also introduced, applying k-Means clustering to consumers’ individual preference correlation vectors. JAR acidity, JAR flavor intensity, and CATA sweetness were found to be primary drivers of liking across the population (p-value < 1e-70). The resulting predictive models demonstrated strong performance even with a limited set of 3 sensory features. Consumers were clustered into two segments with contrasting preferences for 12 different sensory attributes. The proposed analytical pipeline provides a comprehensive approach to sensory and consumer data, enabling both the prediction of general consumer liking and the identification of distinct preference segments.