Novel strategy for establishment of an FT-Raman spectroscopy based quantitative model for poplar holocellulose content determination

Wenli Gao; Ting Shu; Ying Guan; Shengjie Ling; Shengquan Liu; Liang Zhou

doi:10.1016/j.carbpol.2021.118793

Novel strategy for establishment of an FT-Raman spectroscopy based quantitative model for poplar holocellulose content determination

Carbohydr Polym. 2022 Feb 1:277:118793. doi: 10.1016/j.carbpol.2021.118793. Epub 2021 Oct 26.

Authors

Wenli Gao¹, Ting Shu², Ying Guan³, Shengjie Ling², Shengquan Liu⁴, Liang Zhou⁵

Affiliations

¹ School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China.
² School of Physical Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China.
³ School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China.
⁴ School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China. Electronic address: liusq@ahau.edu.cn.
⁵ School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China; Key Lab of State Forest and Grassland Administration on Wood Quality Improvement & High Efficient Utilization, Hefei, Anhui 230036, China. Electronic address: mcyjs1@ahau.edu.cn.

PMID: 34893223
DOI: 10.1016/j.carbpol.2021.118793

Abstract

Raman spectroscopy is effective for studying the ultrastructure, lignin content, and cellulose crystallinity of lignocellulosic materials. However, the quantitative analysis of holocellulose in lignocellulosic materials by this technique is challenging. In this study, based on Fourier-transform Raman (FT-Raman) spectroscopy, a novel strategy for building poplar holocellulose content quantitative model was proposed. Different algorithms were applied, including Principal component regression (PCR), partial least square regression (PLSR), ridge regression (RR), lasso regression (LR), and elastic net regression (ENR). Combined with different algorithms, twelve candidates of internal standard were selected. Sixty models combined by five regression algorithms and twelve internal standards were performed by five-fold cross validation. Consequently, the models constructed through RR, LR, and ENR combined with the internal standard of peak intensity of 2945 cm^-1 were credible (R_p > 0.9, RMSE_p < 1.0, and MAE_p < 0.9). Credible models were obtained, indicating the high potential of FT-Raman spectroscopy for predicting the holocellulose content of lignocellulosic materials.

Keywords: Holocellulose content; Predictive model; Raman spectroscopy; Regularization algorithm.