Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee

Abstract

"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."