林产化学与工业Issue(1)：55-60,6.DOI:10.3969/j.issn.0253-2417.2016.01.008
近红外光谱结合支持向量机快速识别树种
Fast Identification of Wood Species by Near Infrared Spectroscopy Coupling with Support Vector Machine
摘要
Abstract
Fast identification of different wood materials for papermaking by portable hadamard transform near infrared spectroscopy (HT-NIR) in combination with support vector machines (SVM) was investigated in present study. Savitzky-Golay smoothing method and standard normal variation were used to pretreat the spectral for eliminating noise and measurement deviation caused by light scattering. The one-against-all model and one-against-one model were constructed based on different SVM modeling strategies. The prediction performance for genera classification and species classification of two SVM models was evaluated with partial least squares discriminant analysis (PLS-DA). In this study,SVM was applied to identify different wood species, such as eucalyptus, acacia, populus and metasequoia. The genera correct classification rates and species correct classification rates achieved above 98% and 95%, respectively. The SVM method demonstrated its integrated merits in solving complex classification compared with the traditional linear machine learning methods. The study results showed the feasibility of industrial application of NIR technology and laid the foundation for building the on-line NIR analysis system for wood chips.关键词
近红外光谱/支持向量机/树种识别/制浆Key words
near infrared spectroscopy/support vector machines/wood species identification/pulp分类
化学化工引用本文复制引用
梁龙,房桂干,崔宏辉,吴珽,张新民,赵振义..近红外光谱结合支持向量机快速识别树种[J].林产化学与工业,2016,(1):55-60,6.基金项目
国家林业局948技术引进项目(2014-4-31) （2014-4-31）
