Cachaça, a traditional Brazilian spirit, undergoes significant sensory refinement through barrel aging. In this study, we investigated how heat treatment of Brazilian woods (Balsam, Jaqueira, Jequitibá, Amburana, and Ipê) affects the sensory profile of cachaça, using Oak as a benchmark. Physicochemical characterization, toasting assessments, sensory analysis, and artificial intelligence (AI) were integrated to develop a predictive model for optimizing wood selection and heat-treatment conditions to achieve targeted sensory profiles. Applying this model, we produced a five-wood cachaça, a novel spirit distinguished by its complexity and customized sensory attributes. This approach reveals that each wood species develops distinct characteristics depending on toasting parameters such as time and temperature, challenging the current Brazilian practice where a single toasting condition is applied to all woods without prior physicochemical analysis. Linking wood composition with sensory outcomes through AI, this work introduces an unprecedented product innovation and demonstrates the potential of multi-criteria analysis to guide spirit maturation, enhance product design, and reshape the beverage industry.
Reitenbach et al. (Thu,) studied this question.