Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins

Ying Li; Brian K Via; Feifei Han; Yaoxiang Li; Zhiyong Pei

doi:10.3389/fpls.2023.1121287

Comparison of various chemometric methods on visible and near-infrared spectral analysis for wood density prediction among different tree species and geographical origins

Front Plant Sci. 2023 Mar 10:14:1121287. doi: 10.3389/fpls.2023.1121287. eCollection 2023.

Authors

Ying Li¹, Brian K Via², Feifei Han³, Yaoxiang Li⁴, Zhiyong Pei¹

Affiliations

¹ College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, China.
² Forest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, United States.
³ Laboratory Zhejiang Huadong Forestry Engineering Consulting and Design Corporation, Hangzhou, China.
⁴ College of Engineering and Technology, Northeast Forestry University, Harbin, China.

Abstract

Visible and near-infrared (Vis-NIR) spectroscopy has been widely applied in many fields for the qualitative and quantitative analysis. Chemometric techniques including pre-processing, variable selection, and multivariate calibration models play an important role to better extract useful information from spectral data. In this study, a new de-noising method (lifting wavelet transform, LWT), four variable selection methods, as well as two non-linear machine learning models were simultaneously analyzed to compare the impact of chemometric approaches on wood density determination among various tree species and geographical locations. In addition, fruit fly optimization algorithm (FOA) and response surface methodology (RSM) were employed to optimize the parameters of generalized regression neural network (GRNN) and particle swarm optimization-support vector machine (PSO-SVM), respectively. As for various chemometric methods, the optimal chemometric method was different for the same tree species collected from different locations. FOA-GRNN model combined with LWT and CARS deliver the best performance for Chinese white poplar of Heilongjiang province. In contrast, PLS model showed a good performance for Chinese white poplar collected from Jilin province based on raw spectra. However, for other tree species, RSM-PSO-SVM models can improve the performance of wood density prediction compared to traditional linear and FOA-GRNN models. Especially for Acer mono Maxim, when compared to linear models, the coefficient of determination of prediction set ( $R_{p}^{2}$ ) and relative prediction deviation (RPD) were increased by 47.70% and 44.48%, respectively. And the dimensionality of Vis-NIR spectral data was decreased from 2048 to 20. Therefore, the appropriate chemometric technique should be selected before building calibration models.

Keywords: lifting wavelet transform; response surface methodology; variable selection; visible and near infrared spectroscopy; wood density.

Grants and funding

This research was funded by the Outstanding Doctoral Introduction Fund of School, grant number NDYB2020-11; the Natural Science Foundation of Inner Mongolia Autonomous Region, grant number 2021BS03019; and the Science and Technology Project of Inner Mongolia, grant number 2020GG0078.