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

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 (Rp > 0.9, RMSEp < 1.0, and MAEp < 0.9). Credible models were obtained, indicating the high potential of FT-Raman spectroscopy for predicting the holocellulose content of lignocellulosic materials.