|
Estimation of the physical wood properties of Pinus taeda L. radial strips
using least squares support vector machines R.P. Cogdill,a* L.R. Schimleck,b P.D. Jones,b G.F. Peter,c R.F.
Danielsb and A. Clark, IIId aGraduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA 15282, USA. E-
mail: cogdillr@duq.edu bWarnell School of Forest Resources, The University of Georgia, Athens, GA 30602-2152, USA cSchool of Forest
Resources and Conservation, University of Florida, Gainesville, FL, USA dUSDA Forest Service, Southern Research Station, Athens, GA, USA
ABSTRACT:
Near
infrared (NIR) spectroscopy offers a rapid method for estimating many important wood properties, including air-dry density, microfibril angle (MFA) and SilviScan estimated
stiffness (EL(SS)). Wood property calibrations may be improved by using non-linear calibration methods. In this study, we compare calibrations developed using partial least
squares (PLS) regression and least-squares support vector machine (LS-SVM) regression, a relatively new technique for modelling multivariate, non-linear systems. LS-SVM
regression provided the strongest calibration statistics for all wood properties. For an equivalent number of latent variables, the predictive performance of the MFA LS-
SVM calibrations were superior to those of the corresponding PLS calibration, while predictive results for air-dry density and EL(SS) were similar for both calibration
methods.
Keywords: NIR spectroscopy, support vector machines, PLS regression, SilviScan, Pinus taeda, air-dry density, microfibril angle, stiffness
|