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Abstract
To improve the production efficiency in the pulp and paper industry, the chemical composition of pulp wood species has to be measured in real-time, especially the holocellulose and acid insoluble lignin contents. Near infrared (NIR) spectroscopy, as a promising rapid and on-line technology, is an attractive and promising tool to determine holocellulose and lignin contents in pulp wood. Due to the high complexity and nonlinearity of the spectra of pulp wood, it is significant to select suitable chemometric methods. In this study, in order to eliminate noise and irrelevant information of the original spectra collected by a portable spectrometer, four methods were used to preprocess the original spectra, including the first derivative, moving average filtering, multiplicative scatter correction and standard normal variate transformation. Next a comparison was conducted using four modeling approaches, including partial least squares (PLS) regression, least square support vector machine (LSSVM), back-propagation neural network (BPNN), and kernel extreme learning machine (KELM). The last three approaches were calibrated using spectral features that reduced the dimensions by principal component analysis (PCA). Furthermore, regularization parameter and kernel function parameter of LSSVM and KELM were optimized by a particle swarm optimization (PSO) algorithm. The results indicated that multiplicative scatter correction efficiently eliminated the spectral noise and irrelative information, and that KELM displayed the best prediction performance compared to the other approaches. Therefore, an inexpensive and portable NIR spectrometer has been employed to accurately and efficiently determine the chemical composition of pulp wood when combined with multiplicative scatter correction and the KELM method.