Modeling moisture content of sawdust based on NIRS

Based on near infrared spectroscopy (NIRS), the moisture prediction models of single poplar and pine sawdust were constructed, and the spectral data sets of the two sawdust samples were mixed to establish the moisture prediction model, so as to predict the moisture of multi-tree sawdust simultaneously.

In this study, partial least squares regression (PLSR) and support vector regression (SVR) were used to establish a pulpwood sawdust moisture prediction model based on NIRS. Particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to optimize the hyperparameters of the model. Using the sawdust of pulping material as the object of study, the near-infrared spectra of 120 mixed sawdust samples, mainly poplar and pine, were collected. The original spectral data were screened out and preprocessed using the high leverage studentized residual (HLSR) and standard normal variate (SNV) methods, respectively. The uninformative variables elimination (UVE) was used to extract the informative bands. The performance of GWO-SVR, PSO-SVR and PLSR models were compared.

For establishing the NIR prediction model of poplar sawdust moisture, SNV+Auto+SGS, CARS and PSO-SVR were the best optimal preprocessing method, selecting characteristic wavelengths method and modeling methods, respectively ( $R_P^2$=0.916 4, RMSEP=0.114 8%). For establishing the NIR prediction model of pine sawdust moisture, SNV+Auto+SGS combined with UVE, and PSO-SVR were the best pre-processing and modeling methods, respectively ( $R_P^2$=0.934 3, RMSEP=0.063 7%). For establishing the NIR prediction model of mixed sample sets of poplar and pine sawdust moisture, MSC+Auto+SGS combined with UVE, and PSO-SVR were the best pre-processing and modeling methods, respectively ( $R_P^2$=0.922 1, RMSEP=0.111 1%).

NIRS can be used to predict the sawdust moisture content of one single tree species, and it is feasible to construct a prediction model for the sawdust moisture content of multi-tree species in the meanwhile. Model optimization by comparing and screening combinations of different preprocessing and optimization algorithms can significantly improve the accuracy of sawdust moisture NIR estimation models. It provides a theoretical basis and technical support for detecting the moisture of sawdust in real time.