[The online measurement for pulp Kappa number based on near infrared spectroscopy and support vector machine]

A new method for online measurement of pulp Kappa number by means of near infrared diffuse reflectance spectroscopy and support vector machine (SVM) modeling has been developed in this paper. The near infrared diffuse reflectance spectroscopy of 45 Chinese red pine wood pulp samples was acquired. Selecting the absorption rates in 15 vibration absorption peaks of each sample and using dynamic independent component analysis (DICA) to distill the characters of input sample data, the pulp Kappa number predictive model based on SVM was built. From the whole 45 samples, 35 samples was selected to be the calibration set, and the predictive set consisted of the other 10 samples was used to validate the the pulp Kappa number predictive model. The external validation standard deviation is 0.26 for pulp Kappa number predictive model based on SVM, and the determining factor is 0.93 for the model. The internal cross validation standard deviation is 0.22 for pulp Kappa number predictive model based on SVM, and the determining factor is 0.96 for the model. To analyze the effectiveness of SVM method used to build the pulp Kappa number predictive model, the pulp Kappa number predictive model based on linear regression (LR) was also established. The external validation standard deviation is 0.45 for the model based on linear regression (LR), and the determining factor is 0.81 for the model. The internal cross validation standard deviation is 0.41 for the model based on linear regression (LR), and the determining factor is 0.85 for the model. For the 10 test samples, the pulp Kappa number predictive model based on Linear regression (LR) and the model based on SVM all have certain predictive accuracy, but the later higher. The experiment results not only show the feasibility and effectiveness of the near infrared measurement method for pulp Kappa number, but also validate that the pulp Kappa number predictive model based on SVM is more accurate and robust than linear regression model.