Autor(es): Nunes, Cleiton Antônio ; Lima, Claudio Ferreira ; Barbosa, Luiz Cláudio de Almeida ; Colodette, Jorge Luiz ; Fidêncio, Paulo Henrique
Data: 2019
Origem: Oasisbr
Assunto(s): Analytical pyrolysis; Artificial neural network; Least square-support vector machine
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.
