Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/5554 |
Resumo: | The 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. |
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Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning TechniquesAcacia melanoxylonHeartwoodPulp propertiesMultiple Linear RegressionCARTMulti-Layer Perceptron (MLP)Support Vector Machines (SVM)The 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.MDPIRepositório Científico do Instituto Politécnico de Castelo BrancoIglesias, C.Santos, A.J.A.Martínez, J.Pereira, H.Anjos, O.2017-05-15T22:31:01Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/5554engIGLESIAS, C. [et al.] (2017) - Influence of heartwood on wood density and pulp properties explained by machine learning techniques. Forests. ISSN 1999-4907. 8:20.10.3390/f8010020info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T11:44:49Zoai:repositorio.ipcb.pt:10400.11/5554Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:36:18.329457Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
title |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
spellingShingle |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques Iglesias, C. Acacia melanoxylon Heartwood Pulp properties Multiple Linear Regression CART Multi-Layer Perceptron (MLP) Support Vector Machines (SVM) |
title_short |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
title_full |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
title_fullStr |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
title_full_unstemmed |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
title_sort |
Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques |
author |
Iglesias, C. |
author_facet |
Iglesias, C. Santos, A.J.A. Martínez, J. Pereira, H. Anjos, O. |
author_role |
author |
author2 |
Santos, A.J.A. Martínez, J. Pereira, H. Anjos, O. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Iglesias, C. Santos, A.J.A. Martínez, J. Pereira, H. Anjos, O. |
dc.subject.por.fl_str_mv |
Acacia melanoxylon Heartwood Pulp properties Multiple Linear Regression CART Multi-Layer Perceptron (MLP) Support Vector Machines (SVM) |
topic |
Acacia melanoxylon Heartwood Pulp properties Multiple Linear Regression CART Multi-Layer Perceptron (MLP) Support Vector Machines (SVM) |
description |
The 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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-05-15T22:31:01Z 2017 2017-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.11/5554 |
url |
http://hdl.handle.net/10400.11/5554 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IGLESIAS, C. [et al.] (2017) - Influence of heartwood on wood density and pulp properties explained by machine learning techniques. Forests. ISSN 1999-4907. 8:20. 10.3390/f8010020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799130823250673664 |