您好，欢迎访问湖南省农业科学院 机构知识库！

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

文献类型： 外文期刊

作者： Zhu, Xiangrong 1 ; Shan, Yang 1 ; Li, Gaoyang 1 ; Huang, Anmin 3 ; Zhang, Zhuoyong 1 ;

作者机构： 1.Hunan Agr Prod Proc Inst, Changsha 410125, Hunan, Peoples R China

2.Cent S Univ, Coll Chem & Chem Engn, Changsha 410083, Peoples R China

3.Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China

关键词： Wood density;Visible/near-infrared spectrometry;Least squares-support vector machines;Partitioning based on joint x-y distances algorithm

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.098； 五年影响因子：3.464 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis-NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x-y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard-Stone method. as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：湖南省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台