Useful near infrared spectroscopy model calibrations on solid wood samples of Pterocarpus erinaceus (Poir.) for physical, mechanical and colour properties

Pterocarpus erinaceus (Poir.) (local name: Toti, Tem) is a species endemic to the Guinea-Sudan and Sudan-Sahel zones. It is over-exploited for its wood, which is sold in international trade to China and Indian markets. The selection of high performance wood based on wood properties and their vegetative reproduction represents the best option for the establishment of plantations with the aim of promoting the restoration of degraded natural resources. However, measurements of these characters are lengthy, time consuming and destructive, and difficult to perform in Togo because of the lack of equipment. An alternative to those methods is near infrared (NIR) spectroscopy. The aim of this study was to assess the efficiency and feasibility of near infrared spectroscopy for predicting the physical and mechanical properties and the colour parameters of P. erinaceus wood from Burkina Faso, Niger and Togo. The properties were determined by conventional method and correlated with near infrared spectra using partial least square regression. The partial least square regression models were tested by cross-validation and by external validation. The results of this study showed the possible use of near infrared spectroscopy to estimate the physical and mechanical properties and the colour parameters of P. erinaceus wood. In fact, when validating prediction models for radial and tangential shrinkage, the fibre saturation point (FSP) showed R2 values of 0.70, 0.71, and 0.92, respectively, with ratio performance deviation values of 1.84, 1.87 and 3.57. For the colour parameters L*, a* and b*, the R2 values were 0.83, 0.74 and 0.67, respectively, with ratio performance deviation values of 2.40, 2 and 1.70. The results showed that the basic density and fibre saturation point models were classed as good to very good, respectively. Near infrared spectroscopy can therefore be used to predict efficiently the properties of P. erinaceus wood for a large number of samples, enabling the incorporation of those traits in breeding criteria.