Moisture Content Prediction Below and Above Fiber Saturation Point by Partial Least Squares Regression Analysis on Near Infrared Absorption Spectra of Korean Pine


  • Sang-Yun Yang
  • Yeonjung Han
  • Yoon-Seong Chang
  • Kwang-Mo Kim
  • In-Gyu Choi
  • Hwanmyeong Yeo


Near infrared spectroscopy, water absorption band, partial least squares regression, principle component, moisture content measurement


This study was performed to predict the surface moisture content of Korean pine (Pinus koraiensis) with low moisture content (approximately 0%) and high moisture content above the FSP using near IR spectroscopy. Near IR absorbance spectra of circular specimens were acquired at various moisture contents at 25°C. To enhance the precision of the regression model, mathematical preprocessing was performed by determining the three-point moving average and Norris second derivatives. After preprocessing, partial least squares regression was carried out to establish the surface moisture content prediction model. We divided the specimens into two groups based on their moisture contents. For the first group, which possessed moisture contents less than 30%, the R2 values and root mean squared error of prediction (RMSEP) of the model were 0.96 and 1.48, respectively. For the second group, which possessed moisture contents greater than 30%, the R2 values and RMSEP of the model were 0.94 and 4.88, respectively. For all moisture contents, the R2 and RMSEP were 0.96 and 5.15, respectively. As the range of moisture contents included in the prediction model was expanded, the error of the model increased. In addition, the peak positions of the water absorption band (1440 and 1930 nm) shifted to longer wavelengths at higher moisture contents.


Browne F (1957) The penetration of light into wood. Forest Prod J 7:308-314.nCho KC (1998) Application to agricultural study of near infra red spectroscopy. Korean Society for Agricultural Machinery 23(2):195-205.nCzarnik-Matusewicz B, Pilorz S (2006) Study of the temperature-dependent near-infrared spectra of water by two-dimensional correlation spectroscopy and principal components analysis. Vib Spectrosc 40(2):235-245.nEom CD, Han Y, Chang Y, Park JH, Choi JW, Choi IG, 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 Mokchae Konghak 38(6):526-531.nGeladi P, Kowalski BR (1986) Partial least-squares regression: A tutorial. Anal Chimica Acta 185:1-17.nInagaki T, Yonenobu H, Tsuchikawa S (2008) Near-infrared spectroscopic monitoring of the water adsorption/desorption process in modern and archaeological wood. Appl Spectrosc 62(8):860-865.nNorris KH (2001) Applying Norris derivatives. Understanding and correcting the factors which affect diffuse transmittance spectra. NIR News 12(3):6.nRännar S, Lindgren F, Geladi P, Wold S (1994) A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. J Chemometr 8(2):111-125.nThygesen LG, Lundqvist SO (2000) NIR measurement of moisture content in wood under unstable temperature conditions. Part 1. Thermal effects in near infrared spectra of wood. J Near Infrared Spectrosc 8(3):183-190.nTsuchikawa S (2007) A review of recent near infrared research for wood and paper. Appl Spectrosc 42(1):43-71.nTsuchikawa S, Hayashi K, Tsutsumi S (1996a) Nondestructive measurement of the subsurface structure of biological material having cellular structure by using near-infrared spectroscopy. Appl Spectrosc 50(9):1117-1124.nTsuchikawa S, Torii M, Tsutsumi S (1996b) Application of near infrared spectrophotometry to wood, 4: Calibration equations for moisture content. Journal of the Japan Wood Research Society (Japan) 42(8):743-754.n






Research Contributions