Abstract

Determination of wood chemical components such as extrac tives, lignin and car bohydrate content by conventional wet chemistry is time consuming and sometimes hazardous. Near infrared reflectance (NIR) spectroscopy coupled with multivariate calibration was utilised to offer a fast alternative to wet chemistry methods. In this study, 70 Eucalyptus dunnii wood samples were collected to investigate the correlation and modelling potential of using NIR spectra to predict extractives, lignin, carbohydrate content and ash which were determined with classical methods (extractives, ash and lignin) and high-performance liquid chromatography (sugars). Partial least squares regression was used for multivariate calibration. An evaluation of the results found that ash, extractives and lignin could be predicted with the strongest prediction diagnostics while mannose and glucose-to-mannose ratio models exhibited the lowest performance. The robust ability to predict glucose-to-xylose ratio (r² = 0.87) provided a unique way to utilise NIR to monitor biomass quality and could be helpful for the improvement of ethanol and other forest products. The large range in glucose-to-xylose ratio (2.0 to 4.0), as determined through NIR, suggests that using xylose content to estimate total hemicellulose content may be unsuitable, though this type of ratio assumption and analysis is common for softwoods.

© 2016 The Author(s)

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