Prediction of holocellulose and lignin content of pulp wood feedstock using near infrared spectroscopy and variable selection.

Wood is the main feedstock source for pulp and paper industry. However, chemical composition variations from multispecies and multisource feedstock heavily affect the production continuity and stability. As a rapid and non-destructive analysis technique, near infrared (NIR) spectroscopy provides an alternative for wood properties on-line analysis and feedstock quality control. Herein, near infrared spectroscopy coupled with partial least squares (PLS) regression was used to predict holocellulose and lignin contents of various wood species including poplars, eucalyptus and acacias. In order to obtain more accurate and robust prediction models, a comparison was conducted among several variable selection methods for NIR spectral variables optimization, including competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformative variable elimination (MC-UVE), successive projections algorithm (SPA), and genetic algorithm (GA). The results indicated that CARS method displayed relatively higher efficiency over other methods in elimination of uninformative variables as well as enhancement of the predictive performance of models. CARS-PLS models showed significantly higher robustness and accuracy for each property using lowest variable numbers in cross validation and external validation, demonstrating its applicability and reliability for prediction of multispecies feedstock properties.