Abstract
Near infrared (NIR) diffuse reflectance was used for the estimation of air-dry density and basic
density in wood radial strip samples obtained at breast height (1.4 m) from 60 Pinus taeda trees
established in three progeny tests in the south-eastern United States. NIR calibration models were
fitted using raw spectra and pre-processed spectra with second derivative, multiplicative scatter
correction and orthogonal signal correction. Successful calibrations were obtained for both wood
properties using data collected in consecutive 10 mm sections from the samples. Data pre-processing did
not result in model improvements compared to the models fitted using raw data. The effects of using
repeated measures were evaluated by incorporating serial correlation into the partial least squares
regression algorithm. The empirical autocorrelation of the normalised residuals showed that serial
dependence among residuals was successfully removed by using an autoregressive correlation structure of
second order. However, because the initial dependence among observations was not strong, the predictions
were similar using the modified algorithm to those obtained with the traditional approach. These results
indicate that the use of repeated measurements does not represent a serious problem for the development
of NIR calibration models for the prediction of wood properties using radial samples measured in 10 mm
sections and that the specification of the correlation structure may not be required when the models are
used only for predictive purposes.
© 2008 IM Publications LLP
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