Improving spatial synchronization between X-ray and near-infrared spectra information to predict wood density profiles

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. Ana Alves and Andrea Hevia have contributed equally to this work and should be regarded as cofirst authors.

ρ-MtreeRing: A Graphical User Interface for X-ray Microdensity Analysis

“…The ρ-MtreeRing effectiveness was checked based on WinDENDRO™ results. This is a widely used proprietary software, considered a robust tool for microdensity studies [33,34]. Wood cores were collected at breast height with a Pressler borer from six Austrian pines (Pinus nigra Arn.)…”

Influence of Silvicultural Operations on the Growth and Wood Density Properties of Mediterranean Pines

“…Besides the abovementioned wood properties that are desirable for high-quality uses, other characteristics such as wood density are a major physical criterion for wood quality [6] since they are related to many other aspects of quality such as wood strength and shrinkage, fiber properties, and flexibility or stiffness, among others [7,8]. Furthermore, it has been demonstrated that wood density affects carbon storage [9][10][11].…”

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

“…Therefore, rapid and accurate acquisition of wood density is of great significance to modern forestry production. Traditional wood density detection methods include the drainage method, weighing method, and mechanical force-based density detection method ( Alves et al., 2020 ). However, the processes of the above methods are complicated and time-consuming, which are not conducive to the density testing of large quantities of wood in practice.…”