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Journal of Near Infrared Spectroscopy
Volume 12 Issue 4, Pages 263–270 (2004)
doi: 10.1255/jnirs.434

 
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

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