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Chemistry and Industry of Forest Products ›› 2016, Vol. 36 ›› Issue (1): 55-60.doi: 10.3969/j.issn.0253-2417.2016.01.008

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Fast Identification of Wood Species by Near Infrared Spectroscopy Coupling with Support Vector Machine

LIANG Long1, FANG Gui-gan1, CUI Hong-hui1, WU Ting1, ZHANG Xin-min2, ZHAO Zhen-yi2   

  1. 1. Institute of Chemical Industry of Forest Products, CAF;National Engineering Lab. for Biomass Chemical Utilization;Key and Open Lab. of Forest Chemical Engineering, SFA;Key Lab. of Biomass Energy and Material, Jiangsu Province, Nanjing 210042, China;
    2. China Invent Instrument Tech. Co. Ltd., Beijing 100085, China
  • Received:2015-02-02 Online:2016-02-25 Published:2016-03-18

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.

Key words: near infrared spectroscopy, support vector machines, wood species identification, pulp

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