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A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix × eurolepis

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Abstract

Wood density is defined as the ratio of mass to volume and therefore in principle it should be possible to calculate a unique partial least squares regression (PLS-R) model for several species. PLS-R models for wood density based on X-ray microdensity data were calculated for each species Pinus pinaster and Larix × eurolepis and for both species together. After cross-validation and test set validation the data sets were combined and final models were calculated. The common model gave a residual prediction deviation (RPD) of 3.1, a range error ratio (RER) of 11.7, and a SEP/SEC of 1.06. The single models for Pinus pinaster and Larix × eurolepis gave RPD’s of 3.5 and 3.2, RER’s of 13 and 11, and a SEP/SEC of 1.2. To the best knowledge of the authors all obtained PLS-R models are the first ones that fulfil the requirements according to AACC Method 39-00 (AACC in AACC Method, 39-00:15, 1999) to be used at least for screening (RPD ≥ 2.5). Although this method and the defined limits were developed for the analysis of grains they can be used as a rough rule of thumb until limits for wood are available. The improvement of the PLS-R models, compared to published results, might be due to three facts (1) the higher number of scans collected for a single spectrum, (2) that the samples were better represented by the NIR spectra and X-ray microdensity values, and (3) that the sites for the measurement of NIR spectra and X-ray microdensity were coincided as strictly as possibly.

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References

  • AACC (1999) Near-infrared methods—guidelines for model development and maintenance, American Association of Cereal Chemists (AACC). AACC Method 39-00:15

    Google Scholar 

  • Adedipe OE, Dawson-Andoh B (2008) Prediction of yellow-poplar (Liriodendron tulipifera) veneer stiffness and bulk density using near infrared spectroscopy and multivariate calibration. J Near Infrared Spectrosc 16:487–496

    Article  CAS  Google Scholar 

  • Ali M, Emsley AM, Herman H, Heywood RJ (2001) Spectroscopic studies of the ageing of cellulosic paper. Polymer 42:2893–2900

    Article  CAS  Google Scholar 

  • Alves A, Schwanninger M, Pereira H, Rodrigues J (2006) Calibration of NIR to assess lignin composition (H/G ratio) in maritime pine wood using analytical pyrolysis as the reference method. Holzforschung 60:29–31

    Article  CAS  Google Scholar 

  • Bokobza L (2002). Origin of near-infrared absorption bands. In: Siesler YOHW, Kawata S, Heise HM (eds) Near-infrared spectroscopy. Weinheim, Wiley, p 11

  • Bouffier L, Charlot C, Raffin A, Rozenberg P, Kremer A (2008) Can wood density be efficiently selected at early stage in maritime pine (Pinus pinaster Ait.)? Ann Forest Sci 65:106p1–106p8

    Google Scholar 

  • Cown DJ (1978) Comparison of the pilodyn and torsiometer methods for the rapid assessment of wood density in living trees. N Z J For Sci 8:384–391

    Google Scholar 

  • da Silva Perez D, Guillemain A, Alazard P, Plomion C, Rozenberg P, Rodrigues JC, Alves A, Chantre G (2007) Improvement of Pinus pinaster Ait elite trees selection by combining near infrared spectroscopy and genetic tools. Holzforschung 61:611–622

    Article  Google Scholar 

  • Esteves B, Pereira H (2008) Quality assessment of heat-treated wood by NIR spectroscopy Qualitätsbewertung von wärmebehandeltem Holz mittels NIR-Spektroskopie Holz Roh- Werkst 66:323–332

    Google Scholar 

  • Fujimoto T, Yamamoto H, Tsuchikawa S (2007) Estimation of wood stiffness and strength properties of hybrid larch by near-infrared spectroscopy. Appl Spectrosc 61:882–888

    Article  PubMed  CAS  Google Scholar 

  • 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 16:529–537

    Article  CAS  Google Scholar 

  • Gierlinger N, Schwanninger M, Hinterstoisser B, Wimmer R (2002) Rapid determination of heartwood extractives in Larix sp. by means of Fourier transform near infrared spectroscopy. J Near Infrared Spectrosc 10:203–214

    Article  CAS  Google Scholar 

  • Gierlinger N, Jacques D, Grabner M, Wimmer R, Schwanninger M, Rozenberg P, Pâques LE (2004a) Colour of larch heartwood and relationships to extractives and brown-rot decay resistance. Trees Struct Funct 18:102–108

    Article  Google Scholar 

  • Gierlinger N, Schwanninger M, Wimmer R (2004b) Characteristics and classification of Fourier-transform near infrared spectra of the heartwood of different larch species (Larix sp.). J Near Infrared Spectrosc 12:113–119

    Article  CAS  Google Scholar 

  • 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 9:255–261

    Article  CAS  Google Scholar 

  • Guay R, Gagnon R, Morin H (1992) A new automatic and interactive tree-ring measurement system based on a line scan camera. For Chron 68:138–141

    Google Scholar 

  • Hauksson JB, Bergqvist G, Bergsten U, Sjöström M, Edlund U (2001) Prediction of basic wood properties for Norway spruce. Interpretation of Near Infrared Spectroscopy data using partial least squares regression. Wood Sci Technol 35:475–485

    Article  CAS  Google Scholar 

  • Hein PRG, Lima JT, Chaix G (2009) Robustness of models based on near infrared spectra to predict the basic density in Eucalyptus urophylla wood. J Near Infrared Spectrosc 17:141–150

    Article  CAS  Google Scholar 

  • Isik F, Li BL (2003) Rapid assessment of wood density of live trees using the Resistograph for selection in tree improvement programs. Can J For Res-Rev Can Rech For 33:2426–2435

    Article  Google Scholar 

  • Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A III (2005a) Non-destructive estimation of Pinus taeda L. tracheid morphological characteristics for samples from a wide range of sites in Georgia. Wood Sci Technol 39:529–545

    Article  CAS  Google Scholar 

  • Jones PD, Schimleck LR, Peter GF, Daniels RF, Clark A III (2005b) Nondestructive estimation of Pinus taeda L. wood properties for samples from a wide range of sites in Georgia. Can J For Res-Rev Can Rech For 35:85–92

    Article  Google Scholar 

  • Jones PD, Schimleck LR, So CL, Clark A, Daniels RF (2007) High resolution scanning of radial strips cut from increment cores by near infrared spectroscopy. Iawa J 28:473–484

    Google Scholar 

  • Jones PD, Schimleck LR, Daniels RF, Clark A III, Purnell RC (2008) Comparison of Pinus taeda L. whole-tree wood property calibrations using diffuse reflectance near infrared spectra obtained using a variety of sampling options. Wood Sci Technol 42:385–400

    Article  CAS  Google Scholar 

  • Mora CR, Schimleck LR, Isik F (2008) Near infrared calibration models for the estimation of wood density in Pinus taeda using repeated sample measurements. J Near Infrared Spectrosc 16:517–528

    Article  CAS  Google Scholar 

  • Newman RH (2004) Homogeneity in cellulose crystallinity between samples of Pinus radiata wood. Holzforschung 58:91–96

    Article  CAS  Google Scholar 

  • Osborne BG, Fearn T (1998) Near infrared spectroscopy in food analysis. Longman Scientific & Technical, New York, pp 20–41

    Google Scholar 

  • Polge H (1966) Établissement des courbes de variation de la densité du bois par exploration densitométrique de radiographies d’échantillons prélevés à la tarière sur des arbres vivants. Ann For Sci 23:1–206

    Article  Google Scholar 

  • Polge H (1978) Fifteen years of wood radiation densitometry. Wood Sci Technol 12:187–196

    Article  Google Scholar 

  • Rinn F, Schweingruber FH, Schar E (1996) RESISTOGRAPH and X-ray density charts of wood comparative evaluation of drill resistance profiles and X-ray density charts of different wood species. Holzforschung 50:303–311

    Article  Google Scholar 

  • Rozenberg P, Cahalan C (1997) Spruce and wood quality: genetic aspects (a review). Silvae Genetica 46:270–279

    Google Scholar 

  • Rozenberg P, Franc A, Cahalan C (2001) Incorporating wood density in breeding programs for softwoods in Europe: a strategy and associated methods. Silvae Genetica 50:1–7

    Google Scholar 

  • Sanchez-Vargas NM, Sanchez L, Rozenberg P (2007) Plastic and adaptive response to weather events: a pilot study in a maritime pine tree ring. Can J For Res-Rev Can Rech For 37:2090–2095

    Article  Google Scholar 

  • Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639

    Article  CAS  Google Scholar 

  • Schajer GS, Orhan FB (2006) Measurement of wood grain angle, moisture content and density using microwaves. Holz Als Roh-Und Werkstoff 64:483–490

    Article  CAS  Google Scholar 

  • Schimleck LR, French J (2002) Application of NIR spectroscopy to clonal Eucalyptus globulus samples covering a narrow range of pulp yield. Appita J 55:149–154

    CAS  Google Scholar 

  • Schimleck LR, Evans R, Matheson AC (2002) Estimation of Pinus radiata D. Don clear wood properties by near-infrared spectroscopy. J Wood Sci 48:132–137

    Article  Google Scholar 

  • Schimleck L, Evans R, Ilic J (2003a) Application of near infrared spectroscopy to the extracted wood of a diverse range of species. Iawa J 24:429–438

    Google Scholar 

  • Schimleck LR, Mora C, Daniels RF (2003b) Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy. Can J For Res-Rev Can Rech For 33:2297–2305

    Article  Google Scholar 

  • Schimleck LR, Stürzenbecher R, Mora C, Jones PD, Daniels RF (2005) Comparison of Pinus taeda L. wood property calibrations based on NIR spectra from the radial-longitudinal and radial-transverse faces of wooden strips. Holzforschung 59:214–218

    Article  CAS  Google Scholar 

  • Schimleck LR, Downes GM, Evans R (2006a) Estimation of Eucalyptus nitens wood properties by near infrared spectroscopy. Appita J 59:136–141

    Google Scholar 

  • Schimleck LR, Rezende GDSR, Demuner BJ, Downes GM (2006b) Estimation of whole-tree wood quality traits using near infrared spectra from increment cores. Appita J 59:231–236

    CAS  Google Scholar 

  • Schimleck LR, Tyson JA, Jones PD, Peter GF, Daniels RF III, Clark A III (2007) Pinus taeda L. wood property calibrations based on variable numbers of near infrared spectra per core and cores per plantation. J Near Infrared Spectrosc 15:261–268

    Article  CAS  Google Scholar 

  • Schwanninger M, Hinterstoisser B, Gierlinger N, Wimmer R, Hanger J (2004) Application of Fourier transform near infrared Spectroscopy (FT-NIR) to thermally modified wood. Holz Roh- Werkst 62:483–485

    CAS  Google Scholar 

  • Shenk JS, Workman JJ, Westerhaus MO (2001). Application of NIR spectroscopy to agricultural products. In: Burns DA, Ciurczak EW (eds) Handbook of near-infrared analysis. Marcel Dekker Inc., New York, pp 419–474

  • Starr C, Morgan AG, Smith DB (1981) An evaluation of near-infrared reflectance analysis in some plant-breeding programs. J Agric Sci 97:107–118

    Article  Google Scholar 

  • Stefke B, Windeisen E, Schwanninger M, Hinterstoisser B (2008) Determination of the weight percentage gain and of the acetyl group content of acetylated wood by means of different infrared spectroscopic methods. Anal Chem 80:1272–1279

    Article  PubMed  CAS  Google Scholar 

  • Stirling R, Trung T, Breuil C, Bicho P (2007) Predicting wood decay and density using NIR spectroscopy. Wood Fiber Sci 39:414–423

    CAS  Google Scholar 

  • Sykacek E, Gierlinger N, Wimmer R, Schwanninger M (2006) Prediction of natural durability of commercial available European and Siberian larch by near-infrared spectroscopy. Holzforschung 60:643–647

    Article  CAS  Google Scholar 

  • Team RDC (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN: 3-900051-07-0

  • Thygesen LG (1994) Determination of dry matter content and basic density of Norway spruce by near infrared reflectance and transmittance spectroscopy. J Near Infrared Spectrosc 2:127–135

    Article  CAS  Google Scholar 

  • Tsuchikawa S (2007) A review of recent near infrared research for wood and paper. Appl Spectrosc Rev 42:43–71

    Article  CAS  Google Scholar 

  • Tsuchikawa S, Siesler HW (2003) Near-infrared spectroscopic monitoring of the diffusion process of deuterium-labeled molecules in wood. Part I: softwood. Appl Spectrosc 57:667–674

    Article  PubMed  CAS  Google Scholar 

  • Tsuchikawa S, Hirashima Y, Sasaki Y, Ando K (2005) Near-infrared spectroscopic study of the physical and mechanical properties of wood with meso- and micro-scale anatomical observation. Appl Spectrosc 59:86–93

    Article  PubMed  CAS  Google Scholar 

  • Tyson JA, Schimleck LR, Aguiar AM, Abad JIM, Rezende GDSP (2009) Adjusting near infrared wood property calibrations for central Brazil to predict the wood properties of samples from southern Brazil. Appita J 62:46–51

    CAS  Google Scholar 

  • Via BK, So CL, Shupe TF, Stine M, Groom LH (2005) Ability of near infrared spectroscopy to monitor air-dry density distribution and variation of wood. Wood Fiber Sci 37:394–402

    CAS  Google Scholar 

  • Watt MS, Garnett BT, Walker JCF (1996) The use of the pilodyn for assessing outerwood density in New Zealand radiata pine. For Prod J 46:101–106

    Google Scholar 

  • Wielinga B, Raymond CA, James R, Matheson AC (2009) Genetic parameters and genotype by environment interactions for green and basic density and stiffness of Pinus radiata D. Don Estimated using acoustics. Silvae Genetica 58:112–122

    Google Scholar 

  • Williams P, Norris K (2004) Near-Infrared technology in the agricultural and food industries. American Association of Cereal Chemists, Inc., St. Paul, 1-891127-24-1, p 296

  • Workman JJ (1999) Review of process and non-invasive near-infrared and infrared spectroscopy: 1993–1999. Appl Spectrosc Rev 34:1–89

    Article  CAS  Google Scholar 

  • Workman JJ (2001) Infrared and Raman spectroscopy in paper and pulp analysis. Appl Spectrosc Rev 36:139–168

    Article  CAS  Google Scholar 

  • Wüst E, Rudzik L (1996) NIR-Spektroskopische Analytik. In: Günzler MBH, Borsdorf R, Danzer K, Fresenius W, Galensa R, Huber W, Lüderwald I, Schwedt G, Tölg G, Wisser H (eds) Infrarotspektroskopie. Highlight aus dem Analytiker-Taschenbuch. A. Springer, Berlin, pp 221–222

    Google Scholar 

  • Yamashita K, Okada N, Fujiwara T (2007) Use of the Pilodyn for estimating basic density and its applicability to density-based classifying of Cryptomeria japonica green logs. Mokuzai Gakkaishi 53:72–81

    Article  CAS  Google Scholar 

  • Zobel BJ, Jett JB (1995) Genetics of wood production. Springer, Berlin, p xv+337

  • Zobel BJ, van Buijtenen JP (1989) Wood variation: its causes and control. Springer, Berlin, p xv+363

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Acknowledgments

This work was supported by funding from Pessoa 2005–2006 and 2008–2009 (PHC France, FCT Portugal) and by FCT (Portugal), research project (PTDC/AGR-CFL/72606/2006) and the grant holders of two-first authors SFRH/BD/28679/2006 and SFRH/BD/42073/2007. We acknowledge the benefit of obtaining samples from the EU-projects “Towards a European Larch Wood Chain (FAIR 98–3354) and GEMINI: (QLRT-1999-00942)”.

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Authors and Affiliations

  1. Tropical Research Institute of Portugal, Forest and Forest Products Centre, Tapada da Ajuda, 1349-017, Lisbon, Portugal

    Ana Alves, Antonio Santos & José Rodrigues

  2. Centro de Estudos Florestais, Instituto Superior de Agronomia, Tapada Ajuda, 1349-017, Lisbon, Portugal

    Ana Alves & Antonio Santos

  3. INRA, Unité Amélioration Génétique et de Physiologie des Arbres forestiers, CS 40001 Ardon, 45075, Orléans Cedex 2, France

    Philippe Rozenberg, Luc E. Pâques & Jean-Paul Charpentier

  4. Department of Chemistry, BOKU - University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190, Vienna, Austria

    Manfred Schwanninger

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  1. Ana Alves
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  2. Antonio Santos
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  3. Philippe Rozenberg
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  4. Luc E. Pâques
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Correspondence to Manfred Schwanninger.

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Alves, A., Santos, A., Rozenberg, P. et al. A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix × eurolepis . Wood Sci Technol 46, 157–175 (2012). https://doi.org/10.1007/s00226-010-0383-x

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