Abstract
Wood extractives, the non-cell wall components that can be removed by solvents, can play an important role in the protection of the living tree as well as derived wood products. On the other hand they can be detrimental for pulp and paper, paint and varnish films and adhesives. The objective of this work was to develop near infrared-based partial least squares regression models for the prediction of wood extractives. The developed models are well suited for screening of the ethanol and total extractives content of Eucalyptus globulus wood. The models for the prediction of ethanol extractives with residual prediction deviations above 5 are also suited for quality control. It is shown that samples with high extractives content always have a more intense OH combination band than the samples with low extractives content and that near infrared can be used for a rough estimation of the relative performance of the reference methods.
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