Navegando por Autor "Hassemer, Guilherme de Souza"

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Chemical fingerprint of non‐aged artisanal sugarcane spirits using kohonen artifcial neural network.
(2022) Caetano, Daniela; Lima, Clara Mariana Gonçalves; Sanson, Ananda Lima; Silva, Débora Faria; Hassemer, Guilherme de Souza; Verruck, Silvani; Gregório, Sandra Regina; Silva, Gilmare Antônia da; Afonso, Robson José de Cássia Franco; Coutrim, Maurício Xavier; Batiha, Gaber El‐Saber; Gandara, Jesus Simal
This study focuses on the determination of the chemical profle of 24 non-aged Brazilian artisanal sugarcane spirits (cachaça) samples through chromatographic quantifcation and chemometric treatment via principal component analysis (PCA) and Kohonen’s neural network. In total, forty-seven (47) chemical compounds were identifed in the samples of non-aged artisanal cachaça, in addition to determining alcohol content, volatile acidity, and copper. For the PCA of the chemical compounds’ profle, it could be observed that the samples were grouped into seven groups. On the other hand, the variables’ bearings were grouped together, making it difcult to separate the components in relation to the sample groups and reducing the chances of obtaining all the necessary information. However, by using a Kohonen’s neural network, samples were grouped into eight groups. This tool proved to be more accurate in the groups’ formation. Among the chemical classes of the com- pounds observed, esters stood out, followed by alcohols, acids, aldehydes, ketones, phenol, and copper. The abundance of esters in these samples may suggest that these compounds would be part of the regional standard for cachaças produced in the region of Salinas, Minas Gerais.
Descriptive screening and lexicon development of non-aged artisanal cachaça sensorial profile using principal component analysis and Kohonen artificial neural networks.
(2021) Caetano, Daniela; Lima, Clara Mariana Gonçalves; Sanson, Ananda Lima; Silva, Débora Faria; Hassemer, Guilherme de Souza; Verruck, Silvani; Silva, Gilmare Antônia da; Afonso, Robson José de Cássia Franco; Coutrim, Maurício Xavier; Gregório, Sandra Regina
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 prin- cipal components analysis (PCA) and Kohonen's neural network. The attributes (23) were divided between aroma (11) and flavor (12). PCA does not show good dif- ferentiation 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.