Estimation of wood density and chemical composition by means of Diffuse Reflectance Mid-Infrared Fourier Transform (DRIFT-MIR) spectroscopy

Sitka spruce (Picea sitchensis) samples (491) from 50 different clones as well as 24 different tropical hardwoods and 20 Scots pine (Pinus sylvestris) samples were used to construct diffuse reflectance mid-infrared Fourier transform (DRIFT-MIR) based partial least squares (PLS) calibrations on lignin, cellulose, and wood resin contents and densities. Calibrations for density, lignin, and cellulose were established for all wood species combined into one data set as well as for the separate Sitka spruce data set. Relationships between wood resin and MIR data were constructed for the Sitka spruce data set as well as the combined Scots pine and Sitka spruce data sets. Calibrations containing only five wavenumbers instead of spectral ranges 4000−2800 and 1800−700 cm-1 were also established. In addition, chemical factors contributing to wood density were studied. Chemical composition and density assessed from DRIFT-MIR calibrations had R 2 and Q 2 values in the ranges of 0.6−0.9 and 0.6−0.8, respectively. The PLS models gave residual mean squares error of prediction (RMSEP) values of 1.6−1.9, 2.8−3.7, and 0.4 for lignin, cellulose, and wood resin contents, respectively. Density test sets had RMSEP values ranging from 50 to 56. Reduced amount of wavenumbers can be utilized to predict the chemical composition and density of a wood, which should allow measurements of these properties using a hand-held device. MIR spectral data indicated that low-density samples had somewhat higher lignin contents than high-density samples. Correspondingly, high-density samples contained slightly more polysaccharides than low-density samples. This observation was consistent with the wet chemical data.