, Volume 26, Issue 13–14, pp 7695–7716 | Cite as

Predicting the cell-wall compositions of solid Pinus radiata (radiata pine) wood using NIR and ATR FTIR spectroscopies

  • Leona M. Fahey
  • Michél K. Nieuwoudt
  • Philip J. HarrisEmail author
Original Research


Infrared spectroscopy coupled with partial least squares (PLS) regression has been shown to be a rapid alternative to wet chemical analytical methods for determining the cell-wall compositions of wood. Both near infrared (NIR) spectroscopy, and mid-infrared spectroscopy with attenuated total reflectance Fourier transform infrared (ATR FTIR) sampling, coupled with PLS regression, can be used to quickly and accurately predict the lignin contents and monosaccharide compositions of milled wood. However, milling wood can be time consuming and laborious. In this study we demonstrate that PLS-1 models built using NIR and ATR FTIR spectra of milled Pinus radiata wood, with different sized wood particles and different moisture contents, can rapidly and accurately predict the cell-wall compositions of solid wood. A robust assessment of the prediction accuracy was conducted using a separate test set of solid wood samples with both ‘smooth’ and ‘rough’ surface finishes. The lowest standard error (SE) values for most of the compositional predictions were obtained for the ‘rough’ solid wood samples, using PLS-1 models built from NIR spectra of ‘large’ milled wood particles (0.422 mm) with ambient moisture content. The SE achieved for NIR spectroscopy prediction of lignin for the ‘rough’ solid wood was 1.91%, and for the monosaccharides, arabinose (0.37%), xylose (1.25%), galactose (2.00%), mannose (1.54%), and 4-O-methyl glucuronic acid (0.24%). The powerful combination of NIR spectroscopy with PLS regression offers an attractive method for rapid prediction of cell-wall compositions of solid wood samples, thus avoiding milling. In addition, this technique highlights the different levels of these cell-wall components in opposite and compressed regions in solid wood.


Pinus radiata (radiata pine) Solid wood Near infrared (NIR) spectroscopy Attenuated total reflectance Fourier transform infrared (ATR FTIR) spectroscopy Partial least squares (PLS) regression 



We thank Professor John C. F. Walker (School of Forestry, University of Canterbury) for providing the wood samples, and Associate Professor Brian H. McArdle (Department of Statistics, University of Auckland) for statistical advice. This work was supported by the New Zealand Foundation for Research, Science and Technology (now Ministry of Business, Innovation and Employment) [PROJ-12401-PPS-UOC, “Compromised Wood Quality”].

Supplementary material

10570_2019_2659_MOESM1_ESM.pdf (94 kb)
Supplementary material 1 (PDF 94 kb)
10570_2019_2659_MOESM2_ESM.pdf (135 kb)
Supplementary material 2 (PDF 134 kb)
10570_2019_2659_MOESM3_ESM.pdf (135 kb)
Supplementary material 3 (PDF 134 kb)


  1. Baillères H, Davrieux F, Ham-Pichavant F (2002) Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program. Ann For Sci 59:479–490CrossRefGoogle Scholar
  2. Brennan M, McLean JP, Altaner CM, Ralph J, Harris PJ (2012) Cellulose microfibril angles and cell-wall polymers in different wood types of Pinus radiata. Cellulose 19:1385–1404CrossRefGoogle Scholar
  3. Currie HA, Perry CC (2006) Resolution of complex monosaccharide mixtures from plant cell wall isolates by high pH anion exchange chromatography. J Chromatogr 1128:90–96CrossRefGoogle Scholar
  4. Dence CW (1992) The determination of lignin. In: Lin SY, Dence CW (eds) Methods in Lignin Chemistry. Springer series in wood science. Springer, Berlin, pp 33–61CrossRefGoogle Scholar
  5. Esbensen KH, Geladi P, Larsen A (2014) The RPD myth…. NIR News 25:24–28CrossRefGoogle Scholar
  6. Fahey LM, Nieuwoudt MK, Harris PJ (2017) Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies. Cellulose 24:5275–5293CrossRefGoogle Scholar
  7. Fahey LM, Nieuwoudt MK, Harris PJ (2018) Using near infrared spectroscopy to predict the lignin content and monosaccharide compositions of Pinus radiata wood cell walls. Int J Biol Macromol 113:507–514CrossRefPubMedGoogle Scholar
  8. Faix O (1991) Classification of lignins from different botanical origins by FT-IR spectroscopy. Holzforschung 45:21–27CrossRefGoogle Scholar
  9. Faix O, Böttcher JH (1992) The influence of particle size and concentration in transmission and diffuse reflectance spectroscopy of wood. Holz Roh Werkst 50:221–226CrossRefGoogle Scholar
  10. Gilardi G, Abis L, Cass AEG (1995) Carbon-13 CP/MAS solid-state NMR and FT-IR spectroscopy of wood cell wall biodegradation. Enzyme Microb Technol 17:268–275CrossRefGoogle Scholar
  11. Harris PJ (2005) Diversity in plant cell walls. In: Henry RJ (ed) Plant diversity and evolution: genotypic and phenotypic variation in higher plants. CAB International Publishing, Wallingford, pp 201–227CrossRefGoogle Scholar
  12. Harris PJ, Stone BA (2008) Chemistry and molecular organization of plant cell walls. In: Himmel ME (ed) Biomass recalcitrance: deconstructing the plant cell wall for bioenergy. Blackwell Publishing Ltd., Oxford, pp 61–93CrossRefGoogle Scholar
  13. Hein PRG, Lima JT, Chaix G (2010) Effects of sample preparation on NIR spectroscopic estimation of chemical properties of Eucalyptus urophylla S.T. Blake wood. Holzforschung 64:45–54Google Scholar
  14. Isaksson T, Næs T (1988) The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl Spectrosc 42:1273–1284CrossRefGoogle Scholar
  15. Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A III (2006) Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy. Wood Sci Technol 40:709–720CrossRefGoogle Scholar
  16. Kačuráková M, Wilson RH (2001) Developments in mid-infrared FT-IR spectroscopy of selected carbohydrates. Carbohydr Polym 44:291–303CrossRefGoogle Scholar
  17. Lepoittevin C, Rousseau J-P, Guillemin A, Gauvrit C, Besson F, Hubert F, da Silva Perez D, Harvengt L, Plomion C (2011) Genetic parameters of growth, straightness and wood chemistry traits in Pinus pinaster. Ann For Sci 68:873–884CrossRefGoogle Scholar
  18. Poke FS, Raymond CA (2006) Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptus globulus. J Wood Chem Technol 26:187–199CrossRefGoogle Scholar
  19. Ragauskas AJ, Beckham GT, Biddy MJ, Chandra R, Chen F, Davis MF, Davison BH, Dixon RA, Gilna P, Keller M, Langan P, Naskar AK, Saddler JN, Tschaplinski TJ, Tuskan GA, Wyman CE (2014) Lignin valorization: improving lignin processing in the biorefinery. Science 344:1246843CrossRefGoogle Scholar
  20. Rinnan Å, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. Trends Anal Chem 28:1201–1222CrossRefGoogle Scholar
  21. Sandak J, Sandak A, Meder R (2016) Assessing trees, wood and derived products with near infrared spectroscopy: hints and tips. J Near Infrared Spectrosc 24:485–505CrossRefGoogle Scholar
  22. Schwanninger M, Rodrigues JC, Gierlinger N, Hinterstoisser B (2011a) Determination of lignin content in Norway spruce wood by Fourier transformed near infrared spectroscopy and partial least squares regression. Part 1. Wavenumber-selection and evaluation of the selected range. J Near Infrared Spectrosc 19:319–329CrossRefGoogle Scholar
  23. Schwanninger M, Rodrigues JC, Gierlinger N, Hinterstoisser B (2011b) Determination of lignin content in Norway spruce wood by Fourier transformed near infrared spectroscopy and partial least squares regression analysis. Part 2: development and evaluation of the final model. J Near Infrared Spectrosc 19:331–341CrossRefGoogle Scholar
  24. TAPPI (1997) T 264 cm-97. Preparation of wood for chemical analysisGoogle Scholar
  25. TAPPI (1998) T 222 om-98. Acid-insoluble lignin in wood and pulpGoogle Scholar
  26. TAPPI (2009) T 249 cm-00. Carbohydrate composition of extractive-free wood and wood pulp by gas-liquid chromatographyGoogle Scholar
  27. Thumm A, Riddell M, Nanayakkara B, Harrington J, Meder R (2010) Near infrared hyperspectral imaging applied to mapping chemical composition in wood samples. J Near Infrared Spectrosc 18:507–515CrossRefGoogle Scholar
  28. Timell TE (1986) Compression wood in gymnosperms. Springer, BerlinCrossRefGoogle Scholar
  29. Tsuchikawa S, Schwanninger M (2013) A review of recent near-infrared research for wood and paper (part 2). Appl Spectrosc Rev 48:560–587CrossRefGoogle Scholar
  30. Tsuchikawa S, Hayashi K, Tsutsumi S (1996) Nondestructive measurement of the subsurface structure of biological material having cellular structure by using near-infrared spectroscopy. Appl Spectrosc 50:1117–1124CrossRefGoogle Scholar
  31. 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
  32. Zavarin E, Jones SJ, Cool LG (1990) Analysis of solid wood surfaces by diffuse reflectance infrared Fourier transform (drift) spectroscopy. J Wood Chem Technol 10:495–513CrossRefGoogle Scholar
  33. Zhang M, Chavan RR, Smith BG, McArdle BH, Harris PJ (2016) Tracheid cell-wall structures and locations of (1 → 4)-β-D-galactans and (1 → 3)-β-D-glucans in compression woods of radiata pine (Pinus radiata D. Don). BMC Plant Biol 16:1–18CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Biological SciencesThe University of AucklandAucklandNew Zealand
  2. 2.School of Chemical SciencesThe University of AucklandAucklandNew Zealand

Personalised recommendations