, Volume 20, Issue 4, pp 1629–1637 | Cite as

Compositional analysis of Miscanthus giganteus by near infrared spectroscopy

  • Fernanda B. Haffner
  • Valerie D. Mitchell
  • Rebecca A. Arundale
  • Stefan Bauer
Original Paper


Fourier transform near infrared spectroscopy was applied to ball-milled and dried whole plant Miscanthus × giganteus samples in combination with partial least square regression analysis for prediction of main constituents of the biomass. The developed models with 172 calibration samples had an R2 in the range of 0.96–0.99. For the first time, the acetyl content was modeled for Miscanthus. An independent calibration set of 58 samples revealed a low root mean square error of prediction of 0.414 % for extractives, 0.485 % for glucan, 0.249 % for xylan, 0.061 % for arabinan, 0.050 % for acetyl, 0.198 % for Klason lignin, 0.226 % for total ash and 0.133 % for ash after extraction, an indication of a high level of accuracy. The results showed major improvement over previously reported models, which was attributed to the smaller particle size used. The models are a valuable tool for the fast monitoring of the composition of M. × giganteus in e.g. plant breeding studies.


Miscanthus FT-NIR Composition PLS regression Chemometrics 



This work was funded by the Energy Biosciences Institute.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Fernanda B. Haffner
    • 1
  • Valerie D. Mitchell
    • 1
  • Rebecca A. Arundale
    • 2
    • 3
  • Stefan Bauer
    • 1
  1. 1.Energy Biosciences InstituteUniversity of CaliforniaBerkeleyUSA
  2. 2.Department of Plant BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Institute for Genomic BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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