文献类型：期刊文献

中文题名：近红外光谱技术快速识别针叶材和阔叶材的研究

英文题名：Rapid Identification of Softwood and Hardwood by Near Infrared Spectroscopy of Cross-Sectional Surfaces

作者：杨忠[1] 吕斌[1] 黄安民[1] 刘亚娜[1] 谢序勤[1]

第一作者：杨忠

通信作者：Yang, Z[1]

机构：[1]中国林业科学研究院木材工业研究所

年份：2012

卷号：32

期号：7

起止页码：1785-1789

中文期刊名：光谱学与光谱分析

外文期刊名：Spectroscopy and Spectral Analysis

收录：CSTPCD;;EI(收录号：20123215308527);Scopus(收录号：2-s2.0-84864496678);WOS:【SCI-EXPANDED(收录号:WOS:000307429700013)】；北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；PubMed;

基金：国家自然科学基金项目(30800889);国家"948"项目(2003-4-27);北京市自然科学基金项目(6092021)资助

语种：中文

中文关键词：近红外光谱;针叶材;阔叶材;识别;偏最小二乘判别分析法(PLS-DA)

外文关键词：Near infrared spectroscopy （NIR）; Softwood; Hardwood; Identification; Partial least squares discriminant analysis（PLS-DA）

分类号：O657.3;S781

摘要：对一种针叶材和一种阔叶材的横切面采集波长范围为780～2 500nm的近红外漫反射光谱,结合偏最小二乘判别分析法(PLS-DA)对针叶材杉木和阔叶材桉树快速识别的可行性进行了研究,结果表明:(1)利用近红外光谱结合PLS-DA法建立的识别模型对建模样品的识别正确率达到100%,识别模型预测的分类变量值与实际值之间相关系数r达到0.99,SEC为0.07;(2)即使采用短波区域780～1 100nm的近红外光谱也可以获得理想的识别结果(识别正确率为100%),识别模型的r也达到0.99,SEC为0.07;(3)利用近红外光谱建立的识别模型对未知样本的识别正确率都为100%,说明近红外光谱技术可以快速、准确识别针叶材和阔叶材,这为木材识别提供了一种新方法和技术,也为开发低成本的近红外光谱识别仪器提供了科学依据。
The feasibility of wood identification of softwood and hardwood by near infrared spectroscopy （NIR） coupled with partial least squares discriminant analysis （PLS-DA） was investigated in the present paper. The near infrared spectra （780-2 500 nm） were collected from wood cross-section from one softwood species （Chinese fir） and one hardwood species （eucalyptus）. The results show that： （1） The identification accuracy of the calibration samples predicted by the model based on NIR coupled the PLS-DA was 100%. The correlation coefficient between the NIR predicted category variable value and the true value was 0. 990, and the SEC was 0. 071; （2） The identification accuracy by the model based on the spectra with 780-1 100 nm wavelengths also was 100 %, and the correlation coefficient and SEC were 0. 990 and 0. 070, respectively; （3） The identification accu racy for the test samples was 100%. It was suggested that NIR can be used to rapidly and accurately identify softwood and hardwood samples. It also provides a new approach to identifying wood species.