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Chemometrics Approach For Species Identification of Pinus densiflora Sieb. et Zucc. and Pinus densiflora for. erecta Uyeki - Species Classification Using Near-Infrared Spectroscopy in combination with Multivariate Analysis -

소나무와 금강송의 수종식별을 위한 화학계량학적 접근 - 근적외선 분광법과 다변량분석을 이용한 수종 분류 -

Hwang, Sung-Wook;Lee, Won-Hee;Horikawa, Yoshiki;Sugiyama, Junji
황성욱;이원희

  • Received : 2015.06.03
  • Accepted : 2015.07.16
  • Published : 2015.11.25

Abstract

A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

Keywords

wood identification;near-infrared spectroscopy;principal component analysis;partial least square discriminant analysis;Pinus densiflora for. erecta Uyeki;Pinus densiflora Sieb. et Zucc.

References

  1. Antti, H., Sjostrom, M., Wallbacks, L. 1996. Multivariate calibration models using NIR spectroscopy on pulp and paper industrial applications. Journal of Chemometrics 10: 591-603. https://doi.org/10.1002/(SICI)1099-128X(199609)10:5/6<591::AID-CEM474>3.0.CO;2-L
  2. Brunner, M., Eugster, R., Trenka, E., Berganmin-Strotz, L. 1996. FT-NIR spectroscopy and wood identification. Holzforschung 50: 130-134. https://doi.org/10.1515/hfsg.1996.50.2.130
  3. Chang, Y.S., Yang, S.Y., Chung, H., Kang, K.Y., Choi, J.W., Choi, I.G., Yeo, H. 2015. Development of moisture content prediction model for Larix kaempferi sawdust using near infrared spectroscopy. Journal of the Korean Wood Science and Technology 43(3): 304-310. https://doi.org/10.5658/WOOD.2015.43.3.304
  4. Eom, C.D., Han, Y.J., Chang, Y.S., Park, J.H., Choi, J.W., Choi, I.G., Yeo, H. 2010. Evaluation of surface moisture content of Liriodendron tulipifera wood in the hygroscopic range using NIR spectroscopy. Journal of the Korean Wood Science and Technology 38(6): 526-531. https://doi.org/10.5658/WOOD.2010.38.6.526
  5. Hitoshi, Y., Tsuchikawa, S. 2003. Near-Infrared spectroscopic comparison of antique and modern wood. Applied Spectroscopy 57(11): 320-340. https://doi.org/10.1366/000370203322554446
  6. Horikawa, Y., Mizuno-Tazuru, S., Sugiyama, J. 2015. Near-infrared spectroscopy as a potential method for identification of anatomically similar Japanese diploxylons. Journal of Wood Science, Published online.
  7. Lee, S., Lohumi, S., Cho, B.K., Kim, M.S., Lee, S.H. 2014. Development of nondestructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression. Journal of Korean Society for Nondestructive Testing 34(4): 283-289. https://doi.org/10.7779/JKSNT.2014.34.4.283
  8. Lewis, I.R., Daniel, N.W., Chaffin, N.C., Griffiths, P.R. 1994. Raman spectrometry and neural networks for the classification of wood types-1. Spectrochimica Acta Part A: Molecular Spectroscopy 50(11): 1943-1958. https://doi.org/10.1016/0584-8539(94)80207-6
  9. Osborne, B.G., Fearn, T., Hindle, P.H. 1993. (Eds.). Practical NIR spectroscopy with applications in food and beverage analysis. Longman Scientific and Tech. Harlow.
  10. Pastore, T.C.M., Braga, J.W.B., Coradin, V.T.R., Magalhaes, W.L.E., Okino, E.Y.A., Camargos, J.A.A, de Muniz, G.I.B., Bressan, O.A., Davrieux, F. 2011. Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: discrimination of true mahogany, cedar, andiroba, and curupixa. Holzforschung 65: 73-80.
  11. Savitzky, A., Golay, M.J.E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36: 1627-1639. https://doi.org/10.1021/ac60214a047
  12. Schimleck, L.R., Michell, A.J., Vinden, P. 1996. Eucalypt wood classification by NIR spectroscopy and principal components analysis. Appita Journal 49: 319-324.
  13. Schwanninger, M., Rodrigues, J.C., Fackler, K. 2011. A review of band assignments in near infrared spectra of wood and wood components. Journal of Near Infrared Spectroscopy 19: 287-308. https://doi.org/10.1255/jnirs.955
  14. Siesler, H.W., Ozaki, Y., Kawata, S., Heise, H.M. (Eds.). 2008. Near-infrared spectroscopy: principles, instruments, applications. John Wiley & Sons.
  15. Tsuchikawa, S., Inoue, K., Noma, J., Hayashi, K. 2003a. Application of near infrared spectroscopy to wood discrimination. Journal of Wood Science 49: 29-35. https://doi.org/10.1007/s100860300005
  16. Tsuchikawa, S. Siesler, H.W. 2003b. Near-Infrared spectroscopic monitoring of the diffusion process of deuterium-labeled molecules in wood. Part I: Softwood. Applied Spectroscopy 57(6): 187-198. https://doi.org/10.1366/000370203322005463
  17. Tsuchikawa, S., Yonenobu, H., Siesler, H.W. 2005. Near-infrared spectroscopic observation of the ageing process in archaeological wood using a deuterium exchange method. Analyst 130: 379-384. https://doi.org/10.1039/b412759e
  18. Watanabe, K., Abe, H., Kataoka, Y., Nodhito, S. 2011. Species separation of aging and degraded solid wood using near infrared spectroscopy. Japanese Journal of Historical Botany 19: 117-124.
  19. Wold, S., Esbensen, K., Geladi P. 1987. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2: 37-52. https://doi.org/10.1016/0169-7439(87)80084-9
  20. Yang, B.H. 2006. Understanding multivariate data analysis. CommunicationBooks, Seoul, Korea.
  21. Yang, S.Y., Han, Y., Park, J.H., Chung, H., Eom, C.D., Yeo, H. 2015. Moisture content prediction model development for major domestic wood species using near infrared spectroscopy. Journal of the Korean Wood Science and Technology 43(3): 311-319. https://doi.org/10.5658/WOOD.2015.43.3.311

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