Moisture Content Prediction Below and Above Fiber Saturation Point by Partial Least Squares Regression Analysis on Near Infrared Absorption Spectra of Korean Pine
Keywords:Near infrared spectroscopy, water absorption band, partial least squares regression, principle component, moisture content measurement
AbstractThis 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.
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