Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy
نویسندگان
چکیده
Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood by visible near infrared (Vis-NIR) spectroscopy. The objective this study was to select appropriate pre-processing, variable selection multivariate calibration techniques improve accuracy Chinese white poplar (Populus tomentosa carriere) wood. Vis-NIR spectra were de-noised using four (lifting wavelet transform, LWT; WT; multiplicative scatter correction, MSC; standard normal variate, SNV), techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) iteratively retains informative (IRIV), compared simplify dimension high-dimensional spectral matrix. non-linear models generalized regression neural network (GRNN) support vector machine (SVM) performed these selected variables. results showed that best obtained GRNN combined with LWT CARS method (Rp2 = 0.870; RMSEP 13 Kg/m3; RPDp 2.774).
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ژورنال
عنوان ژورنال: Forests
سال: 2022
ISSN: ['1999-4907']
DOI: https://doi.org/10.3390/f13010062