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

Carla Iglesias,António Santos,Ofélia Anjos,Javier Martínez,Helena Pereira

doi:10.3390/f8010020

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.

Highlights

Blackwood and other acacia were introduced in Portugal at the beginning of the twentieth century in order to colonize dry and poor sandy soils along the coast
Transversal stem sections of A. melanoxylon belonging to 20 trees from four sites in Portugal were used—the Camarido National Forest (MNC), at the mouth of Minho River, in the littoral north close to Caminha; the Forest Perimeter of Ovar Dunes (PFDOVM), in the littoral north close to Ovar; the Forest Perimeter of Rebordões de Santa Maria (PFRSM), in the north mid-interior close to Ponte de
Results output variables with the machine learning technique are reported in Table 1, while Table 2 contains a summary of the results

Summary

Introduction

Blackwood and other acacia were introduced in Portugal at the beginning of the twentieth century in order to colonize dry and poor sandy soils along the coast. Some of the species are considered as invasive as they are characterized by vigorous tree growth, root sprouting and fire stimulated seed germination. Given the high timber value of Acacia melanoxylon in its region of origin, a research effort was made to study the wood quality in view of its valorization as a timber species in Europe. Studies were made on heartwood development [1], density and mechanical properties [2]. Blackwood is a medium density hardwood presenting basic density ranging between. The density that is usually higher for heartwood will influence the paper properties [5,6]

Objectives

Methods

Discussion

Conclusion