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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 |
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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.
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Received: 2017-09-22
Accepted: 2018-01-05
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Corresponding Authors:
ZHANG Yi-zhuo
E-mail: Zhangyz@nefu.edu.cn
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[1] ZHANG Yi-zhuo, SU Yao-wen, LI Chao, et al(张怡卓, 苏耀文, 李 超, 等). Journal of Beijing Forestry University(北京林业大学学报), 2016, 38(8): 99.
[2] LIANG Hao, CAO Jun, LIN Xue, et al(梁 浩,曹 军,林 雪,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(7): 2041.
[3] WU Di, WU Hong-xi, CAI Jing-bo, et al(吴 迪, 吴洪喜, 蔡景波, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(6): 423.
[4] XU Hong-mei, WEN Jiang, ZHONG Wen-jie, et al(徐红梅, 文 江, 钟文杰, 等). Journal of Jiangsu University·Natural Science(江苏大学学报·自然科学版), 2017, 38(3):295.
[5] ZONG Jing-xue, YANG Yu-wang, WANG Lei, et al(宗精学, 杨余旺, 王 磊, 等). Jiangsu Journal of Agricultural Sciences(江苏农业学报), 2013, 29(4): 864.
[6] Liang Hao, Cao Jun, Tu Wenjun, et al. BioResources, 2016, 11(3): 7205.
[7] WANG An-xiang, ZHANG Xiao-jun, CAO Yun-hua(王安祥, 张晓军, 曹运华). Infrared and Laser Engineering(红外与激光工程), 2015, 44(11): 3197.
[8] WANG Bo-jin, HUANG Min, ZHU Qi-bing, et al(汪泊锦, 黄 敏, 朱启兵, 等). Acta Photonica Sinica(光子学报), 2011, 40(8): 1132.
[9] WEI Xin-hua, WU Shu, FAN Xiao-dong, et al(魏新华, 吴 姝, 范晓冬, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(3): 242.
[10] ZHANG Yi-zhuo, TU Wen-jun, LI Chao, et al(张怡卓, 涂文俊, 李 超, 等). Journal of Northeast Forestry University(东北林业大学学报), 2016, 44(10): 79. |
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