Chemical Fingerprint of Non-aged Artisanal Sugarcane Spirits Using Kohonen Artificial Neural Network

Ano: 2021

Food Analytical Methods volume 15, pages890–907.


Autores/as: Daniela Caetano, Clara Mariana Gonçalves Lima, Ananda Lima Sanson, Debora Faria Silva, Guilherme de Souza Hassemer, Silvani Verruck, Sandra Regina Gregorio, Gilmare Antonia da Silva, Robson Jose de Cassia Franco Afonso, Mauricio Xavier Coutrim, Gaber El-Saber Batiha & Jesus Simal-Gandara.


This study focuses on the determination of the chemical profile of 24 non-aged Brazilian artisanal sugarcane spirits (cachaça) samples through chromatographic quantification and chemometric treatment via principal component analysis (PCA) and Kohonen’s neural network. In total, forty-seven (47) chemical compounds were identified 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’ profile, it could be observed that the samples were grouped into seven groups. On the other hand, the variables’ bearings were grouped together, making it difficult 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 compounds 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.

Jesús Simal Gándara

Tipo de publicación:
Artigos de impacto