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榆树木材基本密度近红外模型优化的研究
引用本文:李耀翔,徐浩凯.榆树木材基本密度近红外模型优化的研究[J].云南大学学报(自然科学版),2015,37(1):155-162.
作者姓名:李耀翔  徐浩凯
作者单位:东北林业大学 工程技术学院,黑龙江 哈尔滨 150040
基金项目:中央高校基本科研业务费专项(DL12EB07-2);黑龙江省自然科学基金(C201111).
摘    要: 为探究近红外光谱技术野外测量木材基本密度的可行性,用圆盘模拟伐倒木锯面,采集光谱信号,结合偏最小二乘法(PLS)建立榆树木材基本密度预测模型.其校正模型和验证模型决定系数R2分别为0.8456和0.8011,均方根误差RMSE分别为0.0231和0.0266,标准误差SE分别为0.0232和0.0268.为进一步提高模型预测精度,利用卷积平滑、小波变换等6种方法对光谱信号进行预处理.结果表明,基于小波变换去噪的模型精度最好,校正模型和验证模型决定系数分别为0.8996和0.8662,RMSE和SE的值均达到最小.研究表明,近红外光谱技术可用于木材基本密度的野外测量.

关 键 词:榆木  木材基本密度  近红外光谱  小波去噪  野外测量

A study on the optimization of the model of NIR-based elm wood density 
LI Yao-xiang,XU Hao-kai.A study on the optimization of the model of NIR-based elm wood density [J].Journal of Yunnan University(Natural Sciences),2015,37(1):155-162.
Authors:LI Yao-xiang  XU Hao-kai
Institution:College of Engineering and Technology,Northeast Forestry University,Harbin 150040,China
Abstract:The feasibility of using near-infrared spectroscopy (NIR) to measure the basic density of wood in the field was studied in this paper.The discs of elm wood were used as samples to simulate the sawn surface of the felled tree,NIR were collected from the surface of the disc.The NIR prediction model of basic density of elm trees was developed by partial least squares (PLS).The determination coefficient (R2) of the calibration and validation models was 0.8456 and 0.8011,respectively.The root mean square error of calibration (RMSE) was 0.0231and 0.0266 with standard error of 0.0232 and 0.0268.To improve the accuracy of the prediction model,savitzky-golay,wavelet transform and other four pretreatment methods were applied to the raw spectrum.The best model was achieved by wavelet denoising.The determination coefficient (R2) was 0.8996 and 0.8662 for the calibration and validation model,respectively.The RMSE and SE both went to the minimum value.The results showed that NIRS can be used in predicting wood basic density in the field.
Keywords:elm wood  wood basic density  near infrared spectroscopy  wavelet denoising  field measurement
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