Chemical fingerprint of non‐aged artisanal sugarcane spirits using kohonen artifcial neural network.
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2022
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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.
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Traceability, Authenticity, Self-organizing maps
Citação
CAETANO, D. et al. Chemical fingerprint of non‐aged artisanal sugarcane spirits using kohonen artifcial neural network. Food Analytical Methods, v. 15, p. 890–907, 2022. Disponível em: <https://link.springer.com/article/10.1007/s12161-021-02160-8>. Acesso em: 11 out. 2022.