Prediction Method of Wood Bending Strength Based on KF Optimizing NIR
YU Hui-ling1, PAN Shen2, LIANG Yu-liang2, ZHANG Yi-zhuo2*
1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Abstract：The bending strength is an important index to evaluate the mechanical properties of wood, and the rapid and accurate prediction of its nature is a scientific problem with engineering application value. In this paper, the wood bending strength is predicted by near infrared spectroscopy (NIR), combined with Kalman filter (KF) and partial least squares method (PLS). A total of 126 samples of Mongolian oak (Quercus mongolica) were used, and their bending strengths were measured according to the national standard “Wood physical and mechanical properties test method”. In addition, NIR spectra were collected in the wavelengths ranging from 900 to 1 700 nm, and a pretreatment for NIR was carried out by the first order derivative combined with S-G convolution. Then, the spectrum and bending strength samples were considered as a dynamical system, the redundancy spectrum wavelength points were considered as noise signals. Besides, coefficient matrix and standard deviation were acquired by means of KF iteration, and feature selection was achieved by the ratio of coefficient to standard deviation. Finally, the prediction model of wood bending strength was build based on PLS and the selected wavelength points. The result shows that the number of variables is reduced from 117 to 18 after the KF selection, and the correlation coefficient R of the prediction model is 0.81, the root mean square error of prediction (RMSEP) is 6.59. In order to validate the effectiveness of KF, UVE and SPA were used to make a comparison, the correlation coefficient r is improved by 0.05 and 0.16 and the RMSEP is reduced by 2.33 and 7.66 respectively, which can show that KF can be used to select effective spectrum points, reduce the model dimension, and improve the applicability and accuracy of the model.
Key words：Kalman filter; Bending strength; Feature selection; Near infrared spectroscopy; Partial least squares
