Journal of the Korean Wood Science and Technology

The Korean Society of Wood Science & Technology (한국목재공학회)
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Moisture Content Prediction Model Development for Major Domestic Wood Species Using Near Infrared Spectroscopy

근적외선 분광분석법을 이용한 국산 주요 수종의 섬유포화점 이하 함수율 예측 모델 개발

  • Yang, Sang-Yun (Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University) ;
  • Han, Yeonjung (Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University) ;
  • Park, Jun-Ho (Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University) ;
  • Chung, Hyunwoo (Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University) ;
  • Eom, Chang-Deuk (Department of Forest Products, Korea Forest Research Institute) ;
  • Yeo, Hwanmyeong (Department of Forest Sciences, College of Agriculture and Life Sciences, Seoul National University)
  • 양상윤 (서울대학교 농업생명과학대학 산림과학부) ;
  • 한연중 (서울대학교 농업생명과학대학 산림과학부) ;
  • 박준호 (서울대학교 농업생명과학대학 산림과학부) ;
  • 정현우 (서울대학교 농업생명과학대학 산림과학부) ;
  • 엄창득 (국립산림과학원 임산공학부) ;
  • 여환명 (서울대학교 농업생명과학대학 산림과학부)
  • Received : 2015.01.13
  • Accepted : 2015.03.02
  • Published : 2015.05.25
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Abstract

Near infrared (NIR) reflectance spectroscopy was employed to develop moisture content prediction model of pitch pine (Pinus rigida), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), yellow poplar (Liriodendron tulipifera) wood below fiber saturation point. NIR reflectance spectra of specimens ranging from 1000 nm to 2400 nm were acquired after humidifying specimens to reach several equilibrium moisture contents. To determine the optimal moisture contents prediction model, 5 mathematical preprocessing methods (moving average (smoothing point: 3), baseline, standard normal variate (SNV), mean normalization, Savitzky-Golay $2^{nd}$ derivatives (polynomial order: 3, smoothing point: 11)) were applied to reflectance spectra of each specimen as 8 combinations. After finishing mathematical preprocessings, partial least squares (PLS) regression analysis was performed to each modified spectra. Consequently, the mathematical preprocessing methods deriving optimal moisture content prediction were 1) moving average/SNV for pitch pine and red pine, 2) moving average/SNV/Savitzky-golay $2^{nd}$ derivatives for Korean pine and yellow poplar. Every model contained three principal components.

근적외선 반사율 분광분석법을 이용하여 리기다 소나무, 소나무, 잣나무, 백합나무의 섬유포화점 이하 함수율 예측모델을 개발하였다. 시편들을 다양한 평형함수율 상태로 유도한 후 1000 nm~2400 nm 파장영역의 반사율 스펙트럼을 획득하였다. 최적 함수율 예측 모델을 선정하기 위해 5가지의 수학적 전처리(moving average (smoothing point: 3), baseline, standard normal variate (SNV), mean normalization, Savitzky-Golay $2^{nd}$ derivatives (polynomial order: 3, smoothing point: 11))를 8가지 조합으로 각 시편의 반사율 스펙트럼에 적용하였다. 수학적 전처리 후, 변형된 스펙트럼을 이용하여 PLS 회귀분석을 실시하였다. 그 결과, 최적 함수율 예측 모델을 도출한 전처리 방법은 리기다 소나무와 소나무의 경우 moving average/SNV, 잣나무와 백합나무의 경우 moving average/SNV/Savitzky-Golay $2^{nd}$ derivatives이며, 모든 모델은 3개의 주성분을 포함하고 있었다.

Keywords

References

  1. Abdi, H. 2010. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdisciplinary Reviews: Computational Statistics 2(1): 97-106. https://doi.org/10.1002/wics.51
  2. Berbert, P.A., Stenning, B.C. 1996. On-line moisture content measurement of wheat. Journal of agricultural engineering research 65(4): 287-296. https://doi.org/10.1006/jaer.1996.0102
  3. Bucur, V. 2003. Nondestructive characterization and imaging of wood. Springer-Verlag. Berlin.
  4. Burns, D.A., Ciurczak, E.W. 2007. Handbook of near-infrared analysis 3rd ed. CRC press. Boca raton.
  5. Cooper, P.A., Jeremic, D., Radivojevic, S., Ung, Y.T., Leblon, B. 2011. Potential of near-infrared spectroscopy to characterize wood products. Canadian Journal of Forest Research, 41(11): 2150-2157. https://doi.org/10.1139/x11-088
  6. 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
  7. Fujimoto, T., Kobori, H., Tsuchikawa, S. 2012. Prediction of wood density independent of moisture conditions using near infrared spectroscopy. Journal of Near Infrared Spectroscopy 20(3): 353-359. https://doi.org/10.1255/jnirs.994
  8. Hans, G., Leblon, B., Stirling, R., Nader, J., LaRocque, A., Cooper, P. 2013. Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer. The Forestry Chronicle 89(5): 607-620. https://doi.org/10.5558/tfc2013-112
  9. Huisman, J.A., Snepvangers, J.J.J.C., Bouten, W., Heuvelink, G.B.M. 2002. Mapping spatial variation in surface soil water content: comparison of ground-penetrating radar and time domain reflectometry. Journal of Hydrology 269(3): 194-207. https://doi.org/10.1016/S0022-1694(02)00239-1
  10. Jung, H.S., Kang, H.Y., Park, J.H., Lee, N.H., Lee, H.W., Kang, C.W., Yeo, H. 2008. A New Introduction to Wood Drying. Seoul National University Press. Seoul.
  11. Watanabe, K., Mansfield, S.D., Avramidis, S. 2011. Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber. Journal of Wood Science 57(4): 288-294. https://doi.org/10.1007/s10086-011-1181-2
  12. Siesler, H.W., Ozaki, Y., Kawata, S., Heise, H.M. 2008. Near-infrared spectroscopy: principles, instruments, applications. John Wiley & Sons. Weinheim.
  13. 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
  14. Torgovnikov, G.I. 1993. Dielectric properties of wood and wood-based materials. Springer-Verlag. Berlin.
  15. Tsuchikawa, S. 2007. A review of recent near infrared research for wood and paper. Applied Spectroscopy Reviews 42(1): 43-71. https://doi.org/10.1080/05704920601036707
  16. Tsuchikawa, S., Torii, M., Tsutsumi, S. (1996) Application of near infrared spectrophotometry to wood. 4. Calibration equations for moisture content. Mokuzai Gakkaishi 42(8): 743-754.
  17. Williams, P., Norris, K. 2004. Near-infrared technology in the agricultural and food industries 2nd ed. American Association of Cereal Chemists, Inc., Minnesota.
  18. Wold, S., Esbensen, K., Geladi, P. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems 2(1): 37-52. https://doi.org/10.1016/0169-7439(87)80084-9
  19. Yang, S.Y., Han, Y., Chang, Y.S., Kim, K.M., Choi, I.G., Yeo, H. 2013. Moisture Content Prediction Below and Above Fiber Saturation Point by Partial Least Squares Regression Analysis on Near Infrared Absorption Spectra of Korean Pine. Wood and Fiber Science 45(4): 415-422.

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