Development of an electronic nose for characterizing the level of maturation of cachaça - Silvello, Giovanni Casagrande (2021)
Aging in wooden barrels is essential to add quality to cachaça aiming to improve its chemical profile and sensory complexity. The degree of maturation of the distillate is influenced by several factors such as wood species used to manufacture the barrels, application or degree of internal burning, number of times the barrel is used, and aging time. The maturation level of cachaça can be determined using the concentration of aging- marker phenolic compounds extracted from the wood of the barrel and their relations with each other. Since this determination currently depends on costly chromatographic analysis, the periodic monitoring of the level of maturation is impracticable. The development of an electronic nose, an equipment composed of an array of gas sensors, a microcontroller, and a classification algorithm, can make the surveillance of sugar cane spirit aging process economically and operationally feasible for the cachaça industry, producers, and laboratories relying on a simpler analytical structure. This study aimed to characterize the aging process of cachaça using an electronic nose by correlating the results obtained using this equipment with the lignin degradation mechanism during the maturation period of this distilled spirit in wooden barrels. Aged cachaça was studied for up to 60 months of ageing in barrels made from different oak species, with or without internal burning (at different intensities) and three use conditions (new, second use, and extensive use). The aged cachaça was analyzed based on the contents of aging markers (derived from lignin degradation) using high performance liquid chromatography and the \'smellprint\' obtained using the electronic nose prototype sensors array. The innovative technology developed for determining the maturation level of cachaça, based on the electronic nose readings, associated with an artificial neural network algorithm, reached over 90% of accuracy for the classification of samples of different maturation classes. The development of this technology has a good potential to evaluate other characteristics related to the chemical volatile composition and sensory aspects of distilled beverages.