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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2012年第7期1785-1789,共5页
Spectroscopy and Spectral Analysis
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
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)
Near infrared spectroscopy (NIR)
Softwood
Hardwood
Identification
Partial least squares discriminant analysis(PLS-DA)