Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

Aline Regina Walkoff, Sandra Regina Masetto Antunes, Maria Elena Payret Arrúa, Lívia Ramazzoti Chanan Silva, Dionisio Borsato, Paulo Rogério Pinto Rodrigues


Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM) neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization.




Kohonen; pH; segmentation; sugarcane

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Orbital: The Electronic Journal of Chemistry (e-ISSN 1984-6428) is a quarterly scientific journal published by the Institute of Chemistry of the Universidade Federal de Mato Grosso do Sul, Brazil. Orbital is a peer-reviewed, open-access journal.