Abstract
BackgroundWood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry. However, the measurement WBD is time- and cost-consuming, which an alternatively fast and no-destructive measurement is needed. In this study, capability of NIR spectroscopy combined with partial least squares regression (PLSR) to quantify the WBD were examined in multiple wood species. To obtain more accurate and robust prediction models, the grain angle (0° (transverse surface), 45°, 90° (radial surface)) influence on the collection of solid wood spectra and a comparison of found variable selection methods for NIR spectral variables optimization were conducted, including significant Multivariate Correlation (sMC), Regularized elimination procedure (Rep), Iterative predictor weighting (Ipw) and Genetic algorithm (Ga). Models made by random calibration data selection were conducted 200 times performance evaluation.ResultsThese results indicate that 90° angle models display relatively highest efficiency than other angle models, mixed angle model yield a satisfied WBD prediction results as well and could reduce the influence of grain angle. Rep method shows a higher efficiency than other methods which could eliminate the uninformative variables and enhance the predictive performance of 90° angle and mix angle models.ConclusionsThis study is potentially shown that the WBD (g/cm3) on solid wood across grain angles and varies wood species could be measured in a rapid and efficient way using NIR technology. Combined with the PLSR model, our methodology could serve as a tool for wood properties breeding and silviculture study.
Highlights
Wood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry
(1) we tested the capacity of reflectance spectroscopy to characterize the WBD in various of hardwoods tree species using partial least squares regression (PLSR) model, and (2) we focused on the comparison of different grain angle models for the better prediction of WBD, (3) we compared the performance of four variable selection methods, including significant Multivariate Correlation (sMC), Iterative predictor weighting (Ipw), Regularized elimination procedure (Rep) and Genetic algorithm (Ga), for improving the predictive performance of PLS calibrations and to identify the most important wavelength related to WBD
With the high range mean of R2Val (0.77–0.84) and (0.64 to 0.70) and the low range mean of RMSEVal (0.071– 0.074 g/cm3) and (0.05–0.06 g/cm3) that obtained from the 90° angles and mixed angle models respectively, our results clearly shows that the WBD in different types of wood species can be reasonably and accurately predicted using NIR reflectance spectroscopy
Summary
Wood basic density (WBD) is one of the most crucial wood property in tree and mainly determined the end use of wood for industry. Capability of NIR spectroscopy combined with partial least squares regression (PLSR) to quantify the WBD were examined in multiple wood species. To obtain more accurate and robust prediction models, the grain angle (0° (transverse surface), 45°, 90° (radial surface)) influence on the collection of solid wood spectra and a comparison of found variable selection methods for NIR spectral variables optimization were conducted, including significant Multivariate Correlation (sMC), Regularized elimination procedure (Rep), Iterative predictor weighting (Ipw) and Genetic algorithm (Ga). WBD (g/cm3), which is defined as the ratio of its ovendry mass (at 0% moisture) to green volume (water- saturated wood volume), is a critical wood property that highly associate with other wood properties [1] for lots of industrial applications [2].
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