Wood of Near Infrared Spectral Information Extraction Method Research

  • Xue-shun Wang
  • Ting Yang
  • Zhong-jie Lin
  • Fengyan Yang
  • Kaixin Lu
  • Songyue Qiao
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)

Abstract

In order to improve near infrared spectrum analysis precision, it is need to extract information from spectrum data. Derivative can not only eliminate spectrum background disturb , baseline drift and other factors influence, but also improve spectrum resolution ratio. However, at the same time derivative strengthen signal noise, reduce spectrum SNR (signal-to-noise ratio). Smoothing can effectively smooth high frequency noise, heighten spectrum SNR. In use of smoothing window’s size has a great effect on spectrum information extraction. This paper take near infrared spectrum of eucalyptus to research NIR spectrum information extraction methods, pay more attention on smoothing window’s size influence on spectrum information extraction. Then combine with multiple scatter correction and standardized variables to build eucalyptus lignin PLS model. The results showed that, using 1st derivative to combine with one of 19 point smoothing, multiple scatter correction, standardized variables to treat spectrum can all get good modeling result. In one word, spectrum information extraction can dislodge unfavorable factors influence and enhance spectrum analysis precision.

Keywords

Root Mean Square Error High Frequency Noise Eucalyptus Wood Spectrum Information Spectrum Analysis Precision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schimieck, L.R., Anthony, J.M., Carolyn, A.R.: Appita Journal 53(4), 318–322 (2000); 05, 41(4), 177Google Scholar
  2. 2.
    Kelley, S., Hames, B., Meglen, R.: 5th International Biomass Conference of the Americas (2001)Google Scholar
  3. 3.
    Feldhoff, R., Huth-Fehre, T., Cammann, K.: Journal of Near Infrared Sectroscopy 6(A), 171–173 (1998)CrossRefGoogle Scholar
  4. 4.
    Yan, Y.: Near Infrared Spectral Analysis Basic and Application. China Light Industry Press (2005)Google Scholar
  5. 5.
    Huang, A.: Artificial Forest of Chinese Fir Near Infrared Spectrum Charactoristic Forcast. PHD thesism Chinese Academy of Forestry (July 2006)Google Scholar
  6. 6.
    Yang, Z., Jiang, Z., Fei, B., Liu, J.: Application of Near Infrared Spectroscopy to Wood science 20183Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Xue-shun Wang
    • 1
  • Ting Yang
    • 1
  • Zhong-jie Lin
    • 1
  • Fengyan Yang
    • 1
  • Kaixin Lu
    • 1
  • Songyue Qiao
    • 1
  1. 1.College of ScienceBeijing Forestry UniversityBeijingChina

Personalised recommendations