Search

Cachaça is a distilled spirit made from sugarcane, exclusively produced in Brazil, and appreciated worldwide. This paper seeks to evaluate the sensory characteristics of 24 nonaged artisanal cachaça samples from Salinas (Minas Gerais, Brazil) through descriptive analysis, as well as chemometrically treat the obtained data based on principal components analysis (PCA) and Kohonen's neural network. The attributes (23) were divided between aroma (11) and flavor (12). PCA does not show good differentiation of nonaged cachaça samples. On the other hand, by using Kohonen's neural network it was possible to group samples according to their aroma and flavor characteristics in 9 and 10 distinct groups, respectively. A reduced number of descriptors could be used to describe the flavor of cachaça samples (alcohol, acidic, sweet, bitter, citric, tar, and burning), as significant correlations (R > 0.70, p <.05) exist among them with fruity, bagasse, fermented sugarcane juice, and astringent descriptors. This diminution on descriptors numbers could be able to reduce the workload of the judging panel with no losses to the sample' sensory characterization. The use of Kohonen's network chemometric treatment for treat sensory data showed to be a better alternative that PCA approach in this study. Practical applications: The growth in production and the appreciation of cachaça in the domestic and foreign markets have directed the production of the drink in Brazil with a focus on its quality and added value, seeking to obtain international recognition and increase exports. In this sense, the lexicon development and sensory characterization of samples from Salinas‐MG is very important to compare with commercial and artisanal samples from other regions in future studies. This work created a sensory profile for samples from this region, which despite its national and international popularity, does not exist yet. Finally, this is the first study that describes a lexicon development and sensory characterization of cachaças from Salinas‐MG and describes data with artificial neural networks, a potential approach for further studies. [ABSTRACT FROM AUTHOR]