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Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration

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Abstract

Determination of quality parameters such as lignin and extractive content of wood samples by wet chemistry analyses takes a long time. Near-infrared (NIR) spectroscopy coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the NIR spectra, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus brutia Ten. is the most growing pine species in Turkey. Its rotation period is around 80 years; the forest products industry has widely accepted the use of Pinus brutia Ten. because of its ability to grow on a wide range of sites and its suitability to produce desirable products. Pinus brutia Ten. is widely used in construction, window door panel, floor covering, etc. Determination of lignin and extractive content of wood provides information to tree breeders on when to cut and how much chemicals are needed for the pulping and bleaching process. In this study, 58 samples of Pinus brutia Ten. trees were collected in Isparta region of Turkey, and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near-infrared spectrometer between 1,000 and 2,500 nm in diffuse reflectance mode, and multivariate calibration models were built with genetic inverse least squares method for both lignin and extractive content using the concentration information obtained from wet standard reference method. Overall, standard error of calibration (SEC) and standard error of prediction (SEP) ranged between 0.35% (w/w) and 2.40% (w/w).

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References

  • Arnold SA, Crowley J, Vaidyanathan S, Matheson L, Mohan P, Hall JW, Harvey LM, McNeil B (2000) At-line monitoring of a submerged filamentous bacterial cultivation using near infrared spectroscopy. Enzyme Microb Tech 27:691–697

    Article  Google Scholar 

  • Cogdill RP, Schimleck LR, Jones PD (2004) Estimation of the physical wood properties of Pinus taeda L. radial strips using least squares support vector machines. J Near Infrared Spec 12(4):263–269

    Article  CAS  Google Scholar 

  • Delwiche SR (1998) Protein content of single kernels of wheat by near-infrared reflectance spectroscopy. J Cereal Sci 27(3):241–254

    Article  CAS  Google Scholar 

  • DeThomas FA, Hall JW, Monfre SL (1994) Real-time monitoring of polyurethane production using near infrared spectroscopy. Talanta 41:425–431

    Article  CAS  PubMed  Google Scholar 

  • Ferré J, Rius FX (1996) Selection of the best calibration sample subset for multivariate regression. Anal Chem 68:1565–1571

    Article  Google Scholar 

  • Ferrioa JP, Villegasb D, Zarcob J, Apariciob N, Arausc JL, Royob C (2005) Assessment of drum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Res 94(2–3):126–148

    Article  Google Scholar 

  • Hareland GA (1994) Evaluation of flour particle size distribution by laser diffraction, sieve analysis and near-infrared reflectance spectroscopy. J Cereal Sci 20(2):183–190

    Article  Google Scholar 

  • Hedrick SE, Bennett RM, Rials TG (2007) Correlation of near-infrared spectroscopy measurements with the properties of treated wood. J Mater Civil Eng 19(4):279–285

    Article  CAS  Google Scholar 

  • Hibbert DB (1993) Genetic algorithms in chemistry. Chem Intell Lab Syst 19:277–293

    Article  CAS  Google Scholar 

  • 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 40:709–720

    Article  CAS  Google Scholar 

  • Jonsson P, Sjostrom M, Wallbacks L (2004) Strategies for implementation and validation of on-line models for multivariate monitoring and control of wood chip properties. J Chemometr 18(3–4):203–207

    Article  CAS  Google Scholar 

  • Kalivas JH (1997) Two data sets of near infrared spectra. Chem Intell Lab Syst 37(2):255–259

    Article  CAS  Google Scholar 

  • 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 38:257–276

    Article  CAS  Google Scholar 

  • McCaig TN (2002) Extending the use of visible/near-infrared reflectance spectrophotometers to measure colour of food and agricultural products. Food Res Int 35(8):731–736

    Article  CAS  Google Scholar 

  • McClure WF (1994) Near infrared spectroscopy-the giant is running. Anal Chem 66:43A–53A

    Article  CAS  Google Scholar 

  • Miralbés C (2004) Quality control in the milling industry using near infrared transmittance spectroscopy. Food Chem 88:622–628

    Article  Google Scholar 

  • Mosley RM, Williams RR (1998) Determination of the accuracy and efficiency of genetic regression. Appl Spectrosc 52:1197–1202

    Article  CAS  Google Scholar 

  • Özdemir D (2005) Determination of octane number of gasoline using near infrared spectroscopy and genetic multivariate calibration methods. Petroleum Sci Technol 23:1139–1152

    Article  Google Scholar 

  • Özdemir D (2006) Genetic multivariate calibration for near infrared spectroscopic determination of protein, moisture, dry mass, hardness and other residues of wheat. Inter J Food Sci Tech 41(Suppl 2):12–20

    Article  Google Scholar 

  • Özdemir D, Dinç E (2004) Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using uv–visible spectrophotometry and genetic algorithm based multivariate calibration methods. Chem Pharm Bull 52(7):810–817

    Article  PubMed  Google Scholar 

  • Özdemir D, Öztürk B (2004) Genetic multivariate calibration methods for near Infrared (NIR) spectroscopic determination of complex mixtures. Turk J Chem 28:497–514

    Google Scholar 

  • Özdemir D, Williams RR (1999) Multi-instrument calibration in uv-visible spectroscopy using genetic regression. Appl Spectrosc 53:210–217

    Article  Google Scholar 

  • Paradkar RP, Williams RR (1997) Genetic regression as a calibration technique for solid phase extraction of dithizone-metal chelates. Appl Spectrosc 51:92–100

    Article  CAS  Google Scholar 

  • Pizarro MC, Forina M, Casolino MC, Leardi R (1998) Extraction of representative subsets by potential functions methods and genetic algorithms. Chem Intell Lab Syst 40:33–51

    Article  Google Scholar 

  • Poke F, Raymond CA (2006) Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in eucalyptus globulus. J Wood Chem Tech 26:187–199

    Article  CAS  Google Scholar 

  • Punchwein G, Eibelhuber A (1989) Outlier detection in routine analysis of agricultural grain products by near-infrared spectrometry. Anal Chim Acta 223:95–103

    Article  Google Scholar 

  • Schimleck LR, Kube PD, Raymond CA (2006) Extending near infrared reflectance (NIR) pulp yield calibrations to new sites and species. J Wood Chem Technol 26(4):299–311

    Article  CAS  Google Scholar 

  • So CL, Eberhardt TL (2006) Rapid analysis of inner and outer bark composition of Southern Yellow Pine bark from industrial sources. Holz Roh-Werkst 64(6):463–467

    Article  CAS  Google Scholar 

  • Sorvaniemi J, Kinnunen A, Tsados A, Mälkki Y (1993) Using partial least squares regression and multiplicative scatter correction for FT-NIR data evaluation of wheat flours. Food Sci Tech 26(3):251–258

    Google Scholar 

  • Sykes R, Li B, Hodge G, Goldfarb B, Kadla JF, Chang H-M (2005) Prediction of loblolly pine wood properties using transmittance near-infrared spectroscopy. Can J For Res 35:2423–2431

    Article  Google Scholar 

  • Tran CD, Oliveira D, Grishko VI (2004) Determination of enantiomeric compositions of pharmaceutical products by near-infrared spectrometry. Anal Biochem 325:206–214

    Article  CAS  PubMed  Google Scholar 

  • Tsuchikawa S, Hirashima Y, Sasaki Y (2005) Near-infrared spectroscopic study of the physical and mechanical properties of wood with meso- and micro-scale anatomical observation. Appl Spect 59(1):86–93

    Article  CAS  Google Scholar 

  • Yeh T, Yamada T, Capanema E, Chang HM, Chiang V, Kadla JF (2005) Rapid screening of wood chemical component variations using transmittance near-infrared spectroscopy. J Agric Food Chem 53:3328–3332

    Article  CAS  PubMed  Google Scholar 

  • Zobel B, Talbert J (1984) Applied forest tree improvement. Wiley Interscience, New York

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

This project was funded by TUBITAK (The Scientific and Technological Research Council of Turkey Project No 105O524). The authors also thank Isparta Regional Forest Service, Ağlasun Forest Service and Asağıgökdere Forest Service for their support in providing wood samples, and Izmir Institute of Technology for the financial support of purchasing the near-infrared spectrometer.

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Correspondence to B. Üner.

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Üner, B., Karaman, İ., Tanrıverdi, H. et al. Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration. Wood Sci Technol 45, 121–134 (2011). https://doi.org/10.1007/s00226-010-0312-z

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