Abstract
Wood density is one of the most important physical properties of the wood, used in improvement programs for wood quality of major timber species. Traditional core sampling of standing trees has been widely used to assess wood density profiles at high spatial resolution by X-ray microdensitometry methods, but alternative methods to predict wood properties quality are also needed. Near-infrared (NIR) spectroscopy, a non-destructive technique, is being increasingly used for wood property assessment and has already been demonstrated to be able to predict wood density. However, the estimation of wood density profiles by NIR has not yet been extensively studied, and improved models using spectra information (NIR) and X-ray data need to be developed. To this end, partial least square regression (PLS-R) models for predicting wood density were developed at a 1.4 mm spatial resolution on Pinus pinaster wood cores, with an improved spatial synchronization along the tangential and radial directions of the strip, between X-ray data and NIR spectra. The validation of the best model showed a high coefficient of determination (0.95), low error (0.026) and no outlier. Compression wood samples were not detected as outliers and were correctly predicted by the model. However, pith spectra were detected as outliers and its predicted values were overestimated by 33% due to unusual spectra suggesting a diverse chemical composition. The results suggest that NIR-PLS models obtained can be used for screening maritime pine wood density profiles along the radii at 1.4 mm spatial resolution.
Highlights
Wood density has long been recognized as one of the most important wood physical properties
An automated system that combined scanning X-ray microdensitometry with image analysis to assess the wood structure at 50 micrometres level was developed by Evans and co-workers (Evans et al 1995) back in the 90s mainly to assess young softwood plantations (Silviscan-1) (Evans 1994) and later upgraded with an X-ray diffractometer to allow the determination of microfibril angle, and a complex image analysis system that allowed the analysis of hardwood plantations (Silviscan-2) (Anonimous 1997)
The density of compression wood spectra was correctly predicted within the error of the model and the spectra were not flagged as outliers
Summary
Wood density has long been recognized as one of the most important wood physical properties. This species has been under improvement programs initially for straightness and volume followed by improvement in wood quality, including wood density (da Silva Perez et al 2007; Gaspar et al 2009; Pot et al 2002, 2006). Genetic analysis of density components of Pinus pinaster, Pinus nigra and Pinus sylvestris showed that the earlywood density is under stronger and most stable genetic control and most suitable to be included in selection and improvement programs (Louzada and Fonseca 2002; Louzada 2003; Gaspar et al 2008a, b, 2009, 2011; Fernandes et al 2017a,b; Dias et al 2018). For larch, similar heritabilities were found for earlywood and latewood (Pâques et al 2013)
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