Prediction of Basic Density of Wood Chips Using Near-Infrared Spectroscopy and Moisture Content Correction Algorithm

Wood basic density is an important indicator for assessing the pulping properties of raw wood materials. Rapidly determining the basic density of wood chips using near-infrared spectroscopy (NIRS) can provide basic theoretical data for developing and optimising pulp production processes. However, the source complicacy of raw material leads to high variability within the moisture content of wood chips. These fluctuations in the raw material make it difficult for the NIRS model to give a stable prediction performance. In this paper, the moisture desorption process of poplar chips was dynamically monitored by nearinfrared spectroscopy. Principal component analysis (PCA) was applied to distinguish the spectral features due to moisture content to explore the change of free water and bound water in wood fiber. In order to investigate the effect of moisture content on the NIRS prediction of wood density, the partial least square calibration (PLS) models were built using wood chips with different moisture content conditions, respectively. And then external parameter orthogonalization algorithm (EPO) was used to improve the robustness of predictive models by eliminating the influence of chip moisture. The results showed that the best prediction accuracy was obtained from water-saturated chips spectra due to full access to information about fiber structures. However, much water absorption information in the spectra was redundant and useless for modeling, and the variations in moisture content also led to unstable prediction performance. The spectral moisture correction based on EPO was an effective method for desensitizing the calibration model to the influence of moisture content, enabling robust and accurate prediction of basic density. The EPO-PLS model provided a performance with a root mean square error (RMSE) of 12. 23 kg . m determination coefficients (R-2) of 0. 883 4, and residual prediction deviation (RPD) of 2. 93 under different moisture content. This study built a robust NIR calibration model which was robustified against the influence of the variations in moisture content on the wood density prediction. This technology may facilitate the expansion of potential applications of NIR spectroscopy in the paper and pulp industry.