Spectral Pre-Processing and Multivariate Calibration Methods for the Prediction of Wood Density in Chinese White Poplar by Visible and Near Infrared Spectroscopy

Ying Li,Brian K Via,Zhiyong Pei,Gensheng Guo,Yaoxiang Li,Guozhong Wang

doi:10.3390/f13010062

Abstract

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 density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density (Rp2 = 0.870; RMSEP = 13 Kg/m3; RPDp = 2.774).

Highlights

Wood is a porous, complex and heterogeneous organic material
We strived to select the most suitable method for improving the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) samples
This may be due to the fact that different selection strategies among the successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and informative variables (IRIV) methods and different wavelengths were achieved

Summary

Introduction

Changes in wood density result in structural variations at different scales. These molecular, cellular and/or organ variations are strongly associated with the mechanical, physiological and morphological properties of wood [1,2,3]. The accurate prediction of wood density is an important endeavor for the maximization of utility. There exist differences in density among different organs such as branches, trunk and roots [5]. In this situation, it is too time-consuming and expensive to predict wood density with the traditional density-measurement techniques, namely gravimetric means measured in the laboratory. A simple, rapid and non-destructive method is needed for forestry researchers and managers

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Discussion

Conclusion