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Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/4833

Title: Determinação de constituintes químicos em madeira de eucalipto por PI-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte
Other Titles: Determination of chemical constituents in eucalyptus wood by py-gc/ms and multivariate calibration: comparison between artificial neural network and support vector machines
???metadata.dc.creator???: Nunes, Cleiton Antônio
Lima, Claudio Ferreira
Barbosa, Luiz Cláudio de Almeida
Colodette, Jorge Luiz
Fidêncio, Paulo Henrique
Keywords: Analytical pyrolysis
Artificial neural network
Least square-support vector machine
Publisher: Sociedade Brasileira de Química
Issue Date: 27-Aug-2010
Citation: NUNES, C. A. et al. Determinação de constituintes químicos em madeira de eucalipto por PI-CG/EM e calibração multivariada: comparação entre redes neurais artificiais e máquinas de vetor suporte. Química Nova, São Paulo, v. 34, n. 2, p. 279-283, jan./fev. 2011.
Abstract: Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.
URI: http://repositorio.ufla.br/jspui/handle/1/4833
???metadata.dc.language???: pt_BR
Appears in Collections:DCA - Artigos publicados em periódicos

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