Journal of Wood Science

, Volume 56, Issue 6, pp 452–459 | Cite as

Feasibility of near-infrared spectroscopy for online multiple trait assessment of sawn lumber

  • Takaaki Fujimoto
  • Yohei Kurata
  • Kazushige Matsumoto
  • Satoru Tsuchikawa
Original Article


Near-infrared (NIR) spectroscopy coupled with multivariate analysis was applied to estimate multiple traits of sawn lumber. The effects of the lumber conveying speed (LCS) and measurement resolution of spectra (MRS) on the calibrations were examined. NIR spectra ranging from 1300 to 2300 nm were acquired at LCSs of 10, 20, and 30 m/min and at MRSs of 2, 4, and 16 nm. Prediction models of bending strength (F b), modulus of elasticity in bending tests (E b), dynamic modulus of elasticity (E fr), and wood density (DEN) were developed using partial least-squares (PLS) analysis. LCS and MRS did not significantly influence the calibration performance for any wood property. The regression coefficients also showed no clear differences for any of the conditions. This indicates that the important explanatory variables included in the models are not greatly influenced by these measurement conditions. PLS2 analysis results, when presented graphically, allowed easy interpretation of the relationships between wood mechanical properties and chemical components, e.g., bending strength and stiffness were mainly related to polysaccharides cellulose and hemicellulose. NIR spectroscopy has considerable potential for online grading of sawn lumber, despite the harsh measurement conditions.

Key words

Near-infrared spectroscopy Mechanical stress grading Bending strength Cellulose Japanese larch 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kretschmann DE, Green DW (1999) Lumber stress grades and design properties. In: Wood handbook: wood as an engineering material. USDA Forest Service, Madison, WI, p 6.1Google Scholar
  2. 2.
    Hayashi T (2003) Recent developments in processing technology for engineered wood products in Japan. In: See, LS, Hamzah, A, Haron, N, Choon, LS, Fui, LH (eds) Proceedings of The Conference on Forestry and Forest Products Research. Kuala Lumpur, pp 226–237Google Scholar
  3. 3.
    Galligan WL, McDonald KA (2000) Machine grading of lumber: practical concerns for lumber producers. General Technical Report FPLÂ-GTRÂ-7 (Revised), US Department of Agriculture, Madison, WIGoogle Scholar
  4. 4.
    Johansson J, Hagman O, Fjellner BA (2003) Predicting moisture content and density distribution of Scots pine by microwave scanning of sawn timber. J Wood Sci 49:312–316CrossRefGoogle Scholar
  5. 5.
    Burns DA, Ciurczak EW (1992) Handbook of near-infrared analysis. Marcel Dekker, New YorkGoogle Scholar
  6. 6.
    Tsuchikawa S (2007) A review of recent near infrared research for wood and paper. Appl Spectrosc Rev 42:43–71CrossRefGoogle Scholar
  7. 7.
    Thumm A, Meder R (2001) Stiffness prediction of radiata pine clearwood test pieces using near infrared spectroscopy. J Near Infrared Spectrosc 9:117–122CrossRefGoogle Scholar
  8. 8.
    Gindl W, Teischinger A, Schwanninger M, Hinterstoisser B (2001) The relationship between near infrared spectra of radial wood surfaces and wood mechanical properties. J Near Infrared Spectrosc 9:255–261CrossRefGoogle Scholar
  9. 9.
    Kelley SS, Rials TG, Groom LR, So CL (2004) Use of near infrared spectroscopy to predict the mechanical properties of six softwoods. Holzforschung 58:252–260CrossRefGoogle Scholar
  10. 10.
    Fujimoto T, Yamamoto H, Tsuchikawa S (2007) Estimation of wood stiffness and strength properties of hybrid larch by near-infrared spectroscopy. Appl Spectrosc 61:882–888CrossRefPubMedGoogle Scholar
  11. 11.
    Meder R, Thumm A, Marston D (2003) Sawmill trial of at-line prediction of recovered lumber stiffness by NIR spectroscopy of Pinus radiata cants. J Near Infrared Spectrosc 11:137–143CrossRefGoogle Scholar
  12. 12.
    Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S (2008) Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full length lumber specimens. J Near Infrared Spectrosc 16:529–537CrossRefGoogle Scholar
  13. 13.
    Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S (2010) Feasibility of near infrared spectroscopy for online grading technique of sawn lumber with multiple traits. Appl Spectrosc 64:92–99CrossRefPubMedGoogle Scholar
  14. 14.
    Meglen RR, Kelley SS (2002) Use of a region of the visible and near infrared spectrum to predict mechanical properties of wet wood and standing trees. United States Patent Application US2002/0107644 A1Google Scholar
  15. 15.
    International Organization for Standardization (2005) ISO standards, TC165 timber structures, ISO/13910 structural timber - characteristic values of strength-graded timber - sampling, full-size testing and evaluation. ISO, GenevaGoogle Scholar
  16. 16.
    Sobue N (1986) Measurement of Young’s modulus by the transient longitudinal vibration of wooden beams using a fast Fourier transformation spectrum analyzer. Mokuzai Gakkaishi 32:744–747Google Scholar
  17. 17.
    Schimleck LR, Stürzenbecher R, Jones PD, Evans R (2004) Development of wood property calibrations using near infrared spectra having different spectral resolutions. J Near Infrared Spectrosc 12:55–61CrossRefGoogle Scholar
  18. 18.
    Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least-squares procedures. Anal Chem 36:1627–1639CrossRefGoogle Scholar
  19. 19.
    Martens H, Naes T (1993) Multivariate calibration. Wiley, Chichester, pp 116–165Google Scholar
  20. 20.
    Kramer R (1998) Chemometric techniques for quantitative analysis. Marcel Dekker, New York, p 131CrossRefGoogle Scholar
  21. 21.
    Ali M, Emsley AM, Herman H, Heywood RJ (2001) Spectro - scopic studies of the ageing of cellulosic paper. Polymer 42:2893–2900CrossRefGoogle Scholar
  22. 22.
    suchikawa S, Siesler HW (2003) Near-infrared spectroscopic monitoring of the diffusion process of deuterium-labeled molecules in wood, Part.1: softwood. Appl Spectrosc 57:667–674CrossRefGoogle Scholar
  23. 23.
    Mitsui K, Inagaki T, Tsuchikawa S (2008) Monitoring of hydroxyl groups in wood during heat treatment using NIR spectroscopy. Biomacromolecules 9:286–288CrossRefPubMedGoogle Scholar
  24. 24.
    Bokobza L (2002) Origin of near-infrared absorption bands. In: Siesler HW, Ozaki Y, Kawata S, Heise HM (eds) Near-infrared spectroscopy. Wiley-VCH, Weinheim, p 11Google Scholar
  25. 25.
    Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278CrossRefGoogle Scholar
  26. 26.
    Buijs K, Choppin GR (1963) Near-infrared studies of the structure of water, I. Pure water. J Chem Phys 39:2035–2041CrossRefGoogle Scholar
  27. 27.
    Osborne BG, Fearn T (1988) Near infrared spectroscopy in food analysis. Longman Scientific and Technical, Harlow, p 20Google Scholar
  28. 28.
    Williams PC, Sobering DC (1993) Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds, J Near Infrared Spectrosc 1:25–32CrossRefGoogle Scholar
  29. 29.
    Schimleck LR, Doran JC, Rimbawanto A (2003) Near infrared spectroscopy for cost-effective screening of foliar oil characteristics in a Melaleuca cajuputi breeding population. J Agric Food Chem 51:2433–2437CrossRefPubMedGoogle Scholar
  30. 30.
    Chen T, Morris J, Martin E (2007) Gaussian process regression for multivariate spectroscopic calibration. Chemom Intell Lab Syst 87:59–71CrossRefGoogle Scholar
  31. 31.
    Brereton RG (2003) Chemometrics: data analysis for the laboratory and analytical plant. Wiley, Chichester, pp 271–315Google Scholar
  32. 32.
    Dieterle F, Busche S, Gauglitz G (2004) Different approaches to multivariate calibration of nonlinear sensor data. Anal Bioanal Chem 380:383–396CrossRefPubMedGoogle Scholar
  33. 33.
    Vandeginste DGM, Massart DL, Buydens LMC, de Jung S, Lewi PJ, Smeyers-Verbeke J (1998) Handbook of chemometrics, part B. Elsevier, Amsterdam, pp 349–370Google Scholar
  34. 34.
    Delwiche SR, Graybosch RA, Nelson LA, Hruschkal WR (2002) Environmental effects on developing wheat as sensed by near-infrared reflectance of mature grains. Cereal Chem 79:885–891CrossRefGoogle Scholar

Copyright information

© The Japan Wood Research Society 2010

Authors and Affiliations

  • Takaaki Fujimoto
    • 1
  • Yohei Kurata
    • 2
  • Kazushige Matsumoto
    • 1
  • Satoru Tsuchikawa
    • 2
  1. 1.Hokkaido Research OrganizationForest Products Research InstituteAsahikawa, HokkaidoJapan
  2. 2.Graduate School of Bioagricultural SciencesNagoya UniversityNagoyaJapan

Personalised recommendations