Abstract
Wood density is an important criterion for material classification,
as it is directly related to quality of wood for structural use. Several
studies have shown promising results for the estimation of wood density by
near infrared spectroscopy. However, the optimal conditions for spectral
acquisition need to be investigated in order to develop predictive models
and to understand how anisotropy and surface roughness affect the
statistics of predictive partial least square regression models. The aim
of this study was to evaluate how the spectral acquisition technique, wood
surface, and the surface quality influence the ability of partial least
square–based models to estimate wood density. Near infrared
spectra were recorded using an integrating sphere and fiber-optic probe on
the tangential, radial, and transverse surfaces machined by circular and
band saws in 278 wood specimens of six-year-old Eucalyptus hybrids. The
basic density values determined by the conventional method were then
correlated with near infrared spectra acquired using an integrating sphere
and fiber-optic probe on the wood surfaces by means of partial least
square regressions. The most promising models for predicting wood density
were generated from near infrared spectra obtained from the transverse
surface machined by the bandsaw, via an integrating sphere (rp2=0.87,
RMSEP = 23 kg m−3 and RPD = 3.0) as well as for the
optic fiber (rp2=0.78, RMSEP = 35 kg m−3 and RPD =
2.1). Surface quality affected the spectral information and robustness of
predictive models with a rougher surface, caused by band sawing, showing
better results.
© 2018 The Author(s)
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription