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

article

Iglesias, C.
Santos, A.J.A.
Martínez, J.
Pereira, H.
Anjos, O.

MDPI

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

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

Acacia melanoxylon
Heartwood
Pulp properties
Multiple Linear Regression
CART
Multi-Layer Perceptron (MLP)
Support Vector Machines (SVM)

1999-4907;

2017-05-15T22:31:01Z
2017-05-15T22:31:01Z
2017

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.
info:eu-repo/semantics/acceptedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng