Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applications

A goal for the pulp and paper industry is to get a fast and reliable characterization of raw materials as wood and pulp compositions. One possibility for this is near infrared reflectance (NIR) spectroscopy combined with multivariate analysis. In the first part of this study the possibility to make predictions of mixtures of wood chips from three different wood species (Swedish pine, Swedish spruce and Polish pine) is investigated based on NIR spectroscopy. Mixture design and Partial least squares projections to latent structures (PLS) were used for the multivariate calibration modeling. The calculated model was validated both internally and with an external test set. The result was a PLS model with a Q2 = 0.91 according to cross-validation and good prediction of the test set objects, Q2test set = 0·78. In the second part NIR spectroscopy is used to characterize a series of pulp samples. The pulps were also characterized by seventeen traditionally measured pulp properties. PLS was used for the calibration model and internal as well as external validation were done. The resulting PLS model for the 17 pulp properties gave an overall Q2 = 0·61 according to cross-validation. Predictions of the test set objects show that most of the properties are well described by the model.