...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International wood products journal >Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system
【24h】

Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system

机译：在近红外（NIR）光谱系统中使用在线近红外（NIR）的Sugi（Cryptomeria japonica）木材弹性预测模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this study, static bending measurements and online NIR spectra acquisitions were combined to construct modulus of elasticity (MOE) prediction model for sugi lumber. NIR spectra were acquired from tangential surface of sugi lumbers at a speed of 120mmin_l to assess its effectiveness in the wood industry. Cross-validation partial least squares regression (CV-PLSR) and test-set-validation partial least squares regression (TSV-PLSR) analyses were employed for analysing the data. The second derivative(2d) spectra with 19 smoothing points (Savitzky-Golay algorithm, second polynomial) gave the best result as spectral pre-processing treatment with the lowest root mean square error of cross-validation and the highest coefficient of determination for cross-validation based on the optimum number of latent variables as assessed from the minimum validation residual variance value in the CV-PLSR analysis. These 2d spectra were then used in the TSV-PLSR analysis for 100 repetitions to check the robustness ofthe calibration.
机译：在该研究中，组合了静态弯曲测量和在线NIR光谱采集以构建SUGI木材的弹性模量（MOE）预测模型。以120mmin_L的速度从Sugi Lumbers的切向表面获得NIR光谱，以评估其在木材工业中的有效性。使用交叉验证偏最小二乘回归（CV-PLSR）和测试设定验证部分最小二乘回归（TSV-PLSR）分析用于分析数据。具有19平滑点（Savitzky-Golay算法，第二多项式）的第二导数（2D）光谱使得最佳的结果作为频谱预处理处理，具有交叉验证的最低根均方误差和交叉的最高确定系数。根据CV-PLSR分析中的最小验证残差方差值评估的潜在变量的最佳变量最佳数量验证。然后在TSV-PLSR分析中使用这些2D光谱以进行100重复以检查校准的稳健性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号