A correlação entre a composição química e os dados sensoriais de 28 amostras de cachaças foi investigada através de análise de componentes principais (PCA). Um modelo químico usando análise discriminante linear (LDA) para classificar as amostras de cachaças de acordo com suas qualidades sensoriais foi então elaborado. Este modelo apresentou habilidades preditivas de calibração e validação de 87,4 e 100%, respectivamente, e foi capaz de reconhecer corretamente 7 dentre 9 amostras adicionais, apresentando-se como uma ferramenta alternativa potencial para o reconhecimento das qualidades sensoriais de cachaças.

The correlation between the chemical composition and the sensory data for 28 cachaça samples was investigated using principal component analysis (PCA). A chemical model was then developed using linear discriminant analysis (LDA) to classify the distillate samples according to their sensory qualities. This model presented predictive abilities of calibration and validation of 87.4 and 100%, respectively, and was able to recognize correctly 7 out of 9 additional samples according to their sensory evaluations, showing itself as a potential alternative tool of recognizing cachaça sensory qualities.

The hedonic level of commercial cachaças, was evaluated by consumers and by a tasters. The results of sensorial methods analyzed trough Principal Components Analysis, Hierarchical Cluster Analysis and the Pearson linear correlation indicated that the best classified cachaças were produced in copper stills and aged in oak casks. By contrast the worst classified exhibited as the main features be not aged and high alcohol percentage. The index of preference is positively correlated with the intensity of yellow color, wood flavor, sweetness and fruit aroma. There is a negative preference correlation with the acidity, the taste of alcohol and bitterness.