Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.11/5554
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dc.contributor.authorIglesias, C.-
dc.contributor.authorSantos, A.J.A.-
dc.contributor.authorMartínez, J.-
dc.contributor.authorPereira, H.-
dc.contributor.authorAnjos, O.-
dc.date.accessioned2017-05-15T22:31:01Z-
dc.date.available2017-05-15T22:31:01Z-
dc.date.issued2017-
dc.identifier.citationIGLESIAS, C. [et al.] (2017) - Influence of heartwood on wood density and pulp properties explained by machine learning techniques. Forests. ISSN 1999-4907. 8:20.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/5554-
dc.description.abstractThe aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression) and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines) were tested. Classification and regression trees (CART) was the most accurate model for the prediction of pulp ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64–0.75) by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.pt_PT
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relation.ispartofseries1999-4907;-
dc.rightsopenAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAcacia melanoxylonpt_PT
dc.subjectHeartwoodpt_PT
dc.subjectPulp propertiespt_PT
dc.subjectMultiple Linear Regressionpt_PT
dc.subjectCARTpt_PT
dc.subjectMulti-Layer Perceptron (MLP)pt_PT
dc.subjectSupport Vector Machines (SVM)pt_PT
dc.titleInfluence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniquespt_PT
dc.typearticlept_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
degois.publication.firstPage20pt_PT
degois.publication.issue1pt_PT
degois.publication.titleForestspt_PT
dc.peerreviewedyespt_PT
degois.publication.volume8pt_PT
dc.identifier.doi10.3390/f8010020pt_PT
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