Predicting the cell-wall compositions of solid Pinus radiata (radiata pine) wood using NIR and ATR FTIR spectroscopies
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Infrared spectroscopy coupled with partial least squares (PLS) regression has been shown to be a rapid alternative to wet chemical analytical methods for determining the cell-wall compositions of wood. Both near infrared (NIR) spectroscopy, and mid-infrared spectroscopy with attenuated total reflectance Fourier transform infrared (ATR FTIR) sampling, coupled with PLS regression, can be used to quickly and accurately predict the lignin contents and monosaccharide compositions of milled wood. However, milling wood can be time consuming and laborious. In this study we demonstrate that PLS-1 models built using NIR and ATR FTIR spectra of milled Pinus radiata wood, with different sized wood particles and different moisture contents, can rapidly and accurately predict the cell-wall compositions of solid wood. A robust assessment of the prediction accuracy was conducted using a separate test set of solid wood samples with both ‘smooth’ and ‘rough’ surface finishes. The lowest standard error (SE) values for most of the compositional predictions were obtained for the ‘rough’ solid wood samples, using PLS-1 models built from NIR spectra of ‘large’ milled wood particles (0.422 mm) with ambient moisture content. The SE achieved for NIR spectroscopy prediction of lignin for the ‘rough’ solid wood was 1.91%, and for the monosaccharides, arabinose (0.37%), xylose (1.25%), galactose (2.00%), mannose (1.54%), and 4-O-methyl glucuronic acid (0.24%). The powerful combination of NIR spectroscopy with PLS regression offers an attractive method for rapid prediction of cell-wall compositions of solid wood samples, thus avoiding milling. In addition, this technique highlights the different levels of these cell-wall components in opposite and compressed regions in solid wood.
KeywordsPinus radiata (radiata pine) Solid wood Near infrared (NIR) spectroscopy Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy Partial least squares (PLS) regression
We thank Professor John C. F. Walker (School of Forestry, University of Canterbury) for providing the wood samples, and Associate Professor Brian H. McArdle (Department of Statistics, University of Auckland) for statistical advice. This work was supported by the New Zealand Foundation for Research, Science and Technology (now Ministry of Business, Innovation and Employment) [PROJ-12401-PPS-UOC, “Compromised Wood Quality”].
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