Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee - doi: 10.4025/actascitechnol.v34i1.10892

• Autor(a) principal: Bona, Evandro
• Data de Publicação: 2011
• Outros Autores: Silva, Rui Sérgio dos Santos Ferreira da, Borsato, Dionísio, Bassoli, Denisley Gentil
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Acta scientiarum. Technology (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/10892
• topic: self organizing maps; soluble coffee; electronic nose; Tecnologia de Alimentos

The electronic nose (EN) is an instrument very used for food flavor analysis. However, it is also necessary to integrate the equipment with a multivariable pattern recognition system, and to this end the principal component analysis (PCA) is the first choice. Alternatively, self-organizing maps (SOM) had been also suggested, since they are a nonlinear and reliable technique. In this study SOM were used to distinguish soluble coffee according to EN data. The proposed methodology had identified all of the seven coffees evaluated; in addition, the groups and relationships detected were similar to those obtained through PCA. Also, the analysis of network weights allowed gathering the e-nose sensors into 4 groups according to the behavior regarding the samples. Results confirm SOM as an efficient tool to EN data pos-processing, and have showed the methodology as a promising choice for the development of new products and quality control of soluble coffee.