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Forests 2017, 8(1), 20; doi:10.3390/f8010020

Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

1
Department of Natural Resources and Environmental Engineering, University of Vigo, Vigo 36310, Spain
2
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa 1349-017, Portugal
3
Department of Mathematics and Statistics, Centro Universitario de la Defensa, Escuela Naval Militar, Marín 36920, Spain
4
Instituto Politécnico de Castelo Branco, Castelo Branco 6001-909, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Sune Linder and Timothy A. Martin
Received: 7 November 2016 / Revised: 27 December 2016 / Accepted: 31 December 2016 / Published: 6 January 2017
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Abstract

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. View Full-Text
Keywords: Acacia melanoxylon; heartwood; pulp properties; Multiple Linear Regression; CART; Multi-Layer Perceptron (MLP); Support Vector Machines (SVM) Acacia melanoxylon; heartwood; pulp properties; Multiple Linear Regression; CART; Multi-Layer Perceptron (MLP); Support Vector Machines (SVM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Iglesias, C.; Santos, A.J.A.; Martínez, J.; Pereira, H.; Anjos, O. Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques. Forests 2017, 8, 20.

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