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http://dx.doi.org/10.5658/WOOD.2015.43.3.311

Moisture Content Prediction Model Development for Major Domestic Wood Species Using Near Infrared Spectroscopy  

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)
Publication Information
Journal of the Korean Wood Science and Technology / v.43, no.3, 2015 , pp. 311-319 More about this Journal
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.
Keywords
near infrared spectroscopy; partial least squares regression; moisture content prediction;
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Times Cited By KSCI : 1  (Citation Analysis)
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