Wood Science and Technology

, Volume 41, Issue 6, pp 491–499 | Cite as

NIR PLSR model selection for Kappa number prediction of maritime pine Kraft pulps

  • Ana Alves
  • António Santos
  • Denilson da  Silva Perez
  • José Rodrigues
  • Helena Pereira
  • Rogério Simões
  • Manfred Schwanninger
Original
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Abstract

A total of 910 maritime pine (Pinus pinaster Aiton) wood discs, belonging to a genetic trial of 80 families with 11–12 trees per family, were used in this study. A near infrared (NIR) partial least squares regression (PLSR) model for the prediction of Kappa number of Pinus pinaster Aiton pulps obtained from samples pulped under identical conditions was calculated. Very good correlations between NIR spectra of maritime pine pulps and Kappa numbers in the range from 58 to 100 were obtained. Besides the raw spectra, spectra pre-processed with ten methods were used for PLS analysis (cross validation with 57 samples), showing that even after test set validation (with 34 samples) no model decision could be made due to almost identical statistics. The final evaluation that proved the predictive power of the models by predicting pulps with unknown Kappa numbers allowed choosing a model according to a minimal number of outliers found during this process. The minimum–maximum normalized spectra in the wave number range from 6,110 to 5,440 cm−1 used for the calculation gave the best model with a root mean square error of prediction of 2.3 units of Kappa number, a coefficient of determination of 95.9%, and one PLS component. The percentage of outliers during evaluation was 0.9%.

Keywords

Lignin Content Partial Little Square Regression Pinus Pinaster Kappa Number Wave Number Range 
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.
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Notes

Acknowledgments

This work was supported by funding from the EU (research projects GEMINI-QLRT-1999-0942) and Fundação para a Ciência e Tecnologia (Portugal), under POCTI and FEDER programs (research projects POCTI/AGR/33967/99 and POCTI/AGR/47353/2002) and was integrated in the activities of BIOPOL in Centro de Estudos Florestais (Portugal).

References

  1. Antti H, Sjöstrom M, Wallbäcks L (1996) Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applications. J Chemometr 10:591–603CrossRefGoogle Scholar
  2. Antti H, Alexandersson D, Sjöstrom M, Wallbäcks L (2000) Detection of kappa number distributions in kraft pulps using NIR spectroscopy and multivariate calibration. Tappi J 83:102–108Google Scholar
  3. Birkett MD, Gambino MJT (1989) Estimation of pulp Kappa number with Near-Infrared spectroscopy. Tappi J 72:193–197Google Scholar
  4. Celpa (2004) A indústria da pasta. Boletim Estatístico. Celpa. Lisboa, CELPA – Associação da Indústria Papeleira 1:36–46Google Scholar
  5. Easty DB, Berben SA, DeThomas FA, Brimmer PJ (1990) Near-infrared spectroscopy for the analysis of wood pulp: quantifying hardwood-softwood mixtures and estimating lignin content. Tappi J 73:257–261Google Scholar
  6. Fardim P, Ferreira MMC, Duran N (2002) Multivariate calibration for quantitative analysis of eucalypt kraft pulp by NIR spectrometry. J Wood Chem Technol 22:67–81CrossRefGoogle Scholar
  7. 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 10:203–214Google Scholar
  8. Hauksson JB, Bergqvist G, Bergsten U, Sjostrom 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 35:475–485CrossRefGoogle Scholar
  9. Henriksen HC, Naes T, Rodbotten R, Aastveit A (2004) Prediction of important sulphite pulp properties from near infrared spectra: a feasibility study and comparison of methods. J Near Infrared Spectrosc 12:279–285Google Scholar
  10. Hodge GR, Woodbridge WC (2004) Use of near infrared spectroscopy to predict lignin content in tropical and sub-tropical pines. J Near Infrared Spectrosc 12:381–390Google Scholar
  11. Kelley SS, Rials TG, Snell R, Groom LH, Sluiter A (2004) Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood. Wood Sci Technol 38:257–276CrossRefGoogle Scholar
  12. Lindgren T, Edlund U (1998) Prediction of lignin content and pulp yield – from black liquor composition using near-infrared spectroscopy and partial least squares regression. Nord Pulp Pap Res J 13:76–80CrossRefGoogle Scholar
  13. Malkavaara P, Alen R (1998) A spectroscopic method for determining lignin content of softwood and hardwood kraft pulps. Chemometr Intell Lab 44:287–292CrossRefGoogle Scholar
  14. Michell AJ (1995) Pulpwood quality estimation by near-infrared spectroscopic measurements on eucalypt woods. Appita J 48:425–428Google Scholar
  15. Olsson RJO, Tomani P, Karlsson M, Joseffson T, Sjöberg K, Björklund C (1995) Multivariate characterization of chemical and physical descriptors in pulp using NIRR. Tappi J 78:158–166Google Scholar
  16. Schimleck LR, French J (2002) Application of NIR spectroscopy to clonal Eucalyptus globulus samples covering a narrow range of pulp yield. Appita J 55:149–154Google Scholar
  17. Schimleck LR, Wright PJ, Michell AJ, Wallis AFA (1997) Near-infrared spectra and chemical compositions of E-globulus and E-nitens plantation woods. Appita J 50:40–46Google Scholar
  18. Schimleck LR, Payne P, Wearne RH (2005) Determination of important pulp properties of hybrid poplar by near infrared spectroscopy. Wood Fiber Sci 37:462–471Google Scholar
  19. Schultz TP, Burns DA (1990) Rapid secondary analysis of lignocellulose: comparison of near infrared (NIR) and Fourier transform infrared (FTIR). Tappi J 73:209–212Google Scholar
  20. Schwanninger M, Hinterstoisser B (2001) Determination of the lignin content in wood by FT-NIR. In: 11th ISWPC, International symposium on wood and pulping chemistry, Nice, Centre Technique Papeterie III, pp 641–644Google Scholar
  21. Schwanninger M, Hinterstoisser B, Gierlinger N, Wimmer R, Hanger J (2004a) Application of Fourier transform near infrared spectroscopy (FT-NIR) to thermally modified wood. Holz Roh-Werkst 62:483–485Google Scholar
  22. Schwanninger M, Hinterstoisser B, Gradinger C, Messner K, Fackler K (2004b) Examination of spruce wood biodegraded by Ceriporiopsis subvermispora using near and mid infrared spectroscopy. J Near Infrared Spectrosc 12:397–409Google Scholar
  23. Shin SJ, Schroeder LR, Lai YZ (2004) Impact of residual extractives on lignin determination in Kraft pulps. J Wood Chem Technol 24:139–151CrossRefGoogle Scholar
  24. da Silva Perez D, Guillemain A, Chantre G, Alazard P, Alves A, Rodrigues JC, Rozenberg P, Plomion C, Robin E (2005) Improvement of wood, pulp and paper quality of maritime pine (Pinus pinaster Ait.) by combining rapid assessment techniques and genetics. In: International symposium on wood, fibre and pulping chemistry, vol 2. Appita Inc, Auckland, pp 207–214Google Scholar
  25. So CL, Via BK, Groom LH, Schimleck LR, Shupe TF, Kelley SS, Rials TG (2004) Near infrared spectroscopy in the forest products industry. Forest Prod J 54:6–16Google Scholar
  26. Sykes R, Li BL, Hodge G, Goldfarb B, Kadla J, Chang HM (2005) Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy. Can J Forest Res 35:2423–2431CrossRefGoogle Scholar
  27. TAPPI (1999) Kappa number of pulp, test method T 236 om-99. TAPPI Standard Test MethodsGoogle Scholar
  28. Terdwongworakul A, Punsuwan V, Thanapase W, Tsuchikawa S (2005) Rapid assessment of wood chemical properties and pulp yield of Eucalyptus camaldulensis in Thailand tree plantations by near infrared spectroscopy for improving wood selection for high quality pulp. J Wood Sci 51:167–171CrossRefGoogle Scholar
  29. Wallbäcks L, Edlund U, Nordén B, Iversen T (1991) Multivariate characterization of pulp. Part 1: Spectroscopic characterization of physical and chemical differences between pulps using 13C CP/MAS NMR, FT-IR, NIR and multivariate data analysis. Nord Pulp Pap Res J 2:74–80CrossRefGoogle Scholar
  30. Wright JA, Birkett MD, Gambino MJT (1990) Prediction of pulp yield and cellulose content from wood samples using near infrared reflectance spectroscopy. Tappi J 73:164–166Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Ana Alves
    • 1
  • António Santos
    • 1
    • 2
  • Denilson da  Silva Perez
    • 3
  • José Rodrigues
    • 1
    • 4
  • Helena Pereira
    • 1
    • 4
  • Rogério Simões
    • 2
  • Manfred Schwanninger
    • 5
  1. 1.Centro de Estudos Florestais, Instituto Superior de AgronomiaUniversidade Técnica de LisboaLisboaPortugal
  2. 2.Research Unit of Textile and Paper MaterialsUniversidade da Beira InteriorCovilhãPortugal
  3. 3.Laboratoire Bois ProcessAfocel, Domaine UniversitaireGrenoble CedexFrance
  4. 4.Tropical Research Institute of Portugal (IICT), Forest and Forest Products CentreLisboaPortugal
  5. 5.Department of ChemistryBOKU-University of Natural Resources and Applied Life Sciences, ViennaViennaAustria

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