Journal of the Korean Wood Science and Technology

The Korean Society of Wood Science & Technology (한국목재공학회)
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Soft Independent Modeling of Class Analogy for Classifying Lumber Species Using Their Near-infrared Spectra

  • Yang, Sang-Yun (Department of Forest Sciences, Seoul National University) ;
  • Park, Yonggun (Department of Forest Sciences, Seoul National University) ;
  • Chung, Hyunwoo (Department of Forest Sciences, Seoul National University) ;
  • Kim, Hyunbin (Department of Forest Sciences, Seoul National University) ;
  • Park, Se-Yeong (Department of Forest Sciences, Seoul National University) ;
  • Choi, In-Gyu (Department of Forest Sciences, Seoul National University) ;
  • Kwon, Ohkyung (National Instrumentation Center for Environmental Management, Seoul National University) ;
  • Yeo, Hwanmyeong (Department of Forest Sciences, Seoul National University)
  • Received : 2018.12.21
  • Accepted : 2019.01.14
  • Published : 2019.01.25
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Abstract

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

Keywords

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Fig. 1. Raw average NIR absorbance spectra for each species.

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Fig. 2. SNV preprocessed average NIR absorbance spectra for each species.

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Fig. 3. Savitzky-Golay 2nd derivative preprocessed average NIR spectra for each species.

Table 1. The number of lumber samples collected fromseveral National Forestry Cooperative Federations

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Table 2. Optimal number of principal components and explained total variance of principal component analysis model

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Table 3. Confusion matrix in the case of binaryclassification

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Table 4. Confusion matrix of SIMCA based on each species PCA models using raw spectra

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Table 5. Confusion matrix of SIMCA based on each species PCA models using Standard normal variate preprocessed spectra.

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Table 6. Confusion matrix of SIMCA based on each species PCA models using Savitzky-Golay 2nd derivative preprocessed spectra.

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References

  1. Adedipe, O.E., Dawson-Andoh, B., Slahor, J., Osborn, L. 2008. Classification of red oak (Quercus rubra) and white oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies. Journal of Near Infrared Spectroscopy 16(1): 49-57. https://doi.org/10.1255/jnirs.760
  2. Alves, A., Schwanninger, M., Pereira, H., Rodrigues, J. 2006. Calibration of NIR to assess lignin composition (H/G ratio) inmaritime pine wood using analytical pyrolysis as the reference method. Holzforschung 60(1): 29-31. https://doi.org/10.1515/HF.2006.006
  3. Barnes, R.J., Dhanoa, M.S., Lister, S.J. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied spectroscopy 43(5): 772-777. https://doi.org/10.1366/0003702894202201
  4. Blanco, M. Villarroya, I. 2002. NIR spectroscopy: a rapid-response analytical tool. TrAC Trends in Analytical Chemistry 21(4): 240-250. https://doi.org/10.1016/S0165-9936(02)00404-1
  5. Bylesjo, M., Rantalainen, M., Cloarec, O., Nicholson, J.K., Holmes, E., Trygg, J. 2006. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics: A Journal of the Chemometrics Society 20(8-10): 341-351. https://doi.org/10.1002/cem.1006
  6. 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
  7. Cho, B.K., Lohoumi, S., Choi, C., Yang, S.M., Kang, S.G. 2016. Study on Rapid Measurement of Wood Powder Concentration of Wood-Plastic Composites using FT-NIR and FT-IR Spectroscopy Techniques. Journal of the Korean Wood Science and Technology 44(6): 852-863. https://doi.org/10.5658/WOOD.2016.44.6.852
  8. 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
  9. Esbensen, K.H., Guyot, D., Westad, F., Houmoller, L.P. 2002. Multivariate data analysis-in practice: an introduction to multivariate data analysis and experimental design 5th edition. Camo Process AS.
  10. Fujimoto, T., Tsuchikawa, S. 2010. Identification of dead and sound knots by near infrared spectroscopy. Journal of Near Infrared Spectroscopy 18(6): 473-479. https://doi.org/10.1255/jnirs.887
  11. Hafemann, L.G., Oliveira, L.S., Cavalin, P. 2014. Forest Species Recognition Using Deep Convolutional Neural Networks. 22nd International Conference on Pattern Recognition (ICPR). pp. 1103-1107.
  12. Hermanson, J.C., Wiedenhoeft, A.C. 2011. A brief review of machine vision in the context of automated wood identification systems. IAWA Journal 32(2): 233-250. https://doi.org/10.1163/22941932-90000054
  13. Horvath, L., Peszlen, I., Peralta, P., Kelley, S. 2011. Use of transmittance near-infrared spectroscopy to predict the mechanical properties of 1-and 2-year-old transgenic aspen. Wood Science and Technology 45(2): 303-314. https://doi.org/10.1007/s00226-010-0330-x
  14. Hwang, S.W., Lee, W.H., Horikawa, Y., Sugiyama, J. 2015. 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. Journal of the Korean Wood Science and Technology 43(6): 701-713. https://doi.org/10.5658/WOOD.2015.43.6.701
  15. Jiang, Z.H., Huang, A.M., Wang, B. 2006. Near infrared spectroscopy of wood sections and rapid density prediction. Spectroscopy and Spectral Analysis 26(6): 1034-1037. https://doi.org/10.3321/j.issn:1000-0593.2006.06.015
  16. Kwon, O., Lee, H. G., Lee, M. R., Jang, S., Yang, S. Y., Park, S. Y., Choi, I. G, Yeo, H. 2017. Automatic Wood Species Identification of Korean Softwood Based on Convolutional Neural Networks. Journal of the Korean Wood Science and Technology 45(6): 797-808. https://doi.org/10.5658/WOOD.2017.45.6.797
  17. Nisgoski, S., de Oliveira, A.A., de Muniz, G.I.B. 2017. Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra. Wood Science and Technology 51(4): 929-942. https://doi.org/10.1007/s00226-017-0915-8
  18. Park, S.Y., Kim, J.C., Kim, J.H., Yang, S.Y., Kwon, O., Yeo, H., Cho, K.C., Choi, I.G. 2017. Possibility of Wood Classification in Korean Softwood Species Using Near-infrared Spectroscopy Based on Their Chemical Compositions. Journal of the Korean Wood Science and Technology 45(2): 202-212. https://doi.org/10.5658/WOOD.2017.45.2.202
  19. Pasquini, C. 2003. Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society 14(2): 198-219. https://doi.org/10.1590/S0103-50532003000200006
  20. Porep, J.U., Kammerer, D.R., Carle, R. 2015. On-line application of near infrared (NIR) spectroscopy in food production. Trends in Food Science and Technology 46(2): 211-230. https://doi.org/10.1016/j.tifs.2015.10.002
  21. Russ, A., Fiserova, M., Gigac, J. 2009. Preliminary study of wood species identification by NIR spectroscopy. Wood Research 54(4): 23-32.
  22. Savitzky, A., Golay, M.J. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry 36(8): 1627-1639. https://doi.org/10.1021/ac60214a047
  23. Schimleck, L.R. Evans, R. 2003. Estimation of air-dry density of increment cores by near infrared spectroscopy. Appita Journal 56(4): 312-317.
  24. Sokolova, M., Lapalme, G. 2009. A systematic analysis of performance measures for classification tasks. Information Processing & Management 45(4): 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
  25. Thumm, A., Meder, R. 2001. Stiffness prediction of radiata pine clearwood test pieces using near infrared spectroscopy. Journal of Near Infrared Spectroscopy 9(2): 117-122. https://doi.org/10.1255/jnirs.298
  26. Thygesen, L.G., Lundqvist, S.O. 2000. NIR measurement of moisture content in wood under unstable temperature conditions. Part 1. Thermal effects in near infrared spectra of wood. Journal of Near Infrared Spectroscopy 8(3): 183-189. https://doi.org/10.1255/jnirs.277
  27. Uner, B., Karaman, I., Tanriverdi, H., Ozdemir, D. 2011. Determination of lignin and extractive content of Turkish Pine (Pinus brutia Ten.) trees using near infrared spectroscopy and multivariate calibration. Wood Science and Technology 45(1): 121-134. https://doi.org/10.1007/s00226-010-0312-z
  28. Watanabe, A., Morita, S., Ozaki, Y. 2006. A study on water adsorption onto microcrystalline cellulose by near-infrared spectroscopy with two-dimensional correlation spectroscopy and principal component analysis. Applied Spectroscopy 60(9): 1054-1061. https://doi.org/10.1366/000370206778397452
  29. Wold, S. 1976. Pattern recognition by means of disjoint principal components models. Pattern recognition 8(3): 127-139. https://doi.org/10.1016/0031-3203(76)90014-5
  30. Yang, I.C., Tsai, C.Y., Hsieh, K.W., Yang, C.W., Ouyang, F., Lo, Y.M., Chen, S. 2013. Integration of SIMCA and near-infrared spectroscopy for rapid and precise identification of herbal medicines. Journal of Food and Drug Analysis 21(3): 268-278. https://doi.org/10.1016/j.jfda.2013.07.008
  31. 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
  32. Yang, S.Y., Park, Y.G., Chung, H.W., Kim, H.B., Park, S.Y., Choi, I.G., Kwon, O., Cho, K.C., Yeo, H. 2017. Partial Least Squares Analysis on Near-Infrared Absorbance Spectra by Air-dried Specific Gravity of Major Domestic Softwood Species. Journal of the Korean Wood Science and Technology 45(4): 399-408. https://doi.org/10.5658/WOOD.2017.45.4.399
  33. Zhao, R.J., Huo, X.M., Zhang, L. 2009. Estimation of modulus of elasticity of Eucalyptus pellita wood by near infrared spectroscopy. Spectroscopy and Spectral Analysis 29(9): 2392-2395. https://doi.org/10.3964/j.issn.1000-0593(2009)09-2392-04