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Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part I: Lignin
Published on: March 11, 2010
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Complementary Raman and FTIR spectroscopic insights into lignin removal from poplar using deep eutectic solvents
Penghui Li1, Honghong Wang2, Weicheng Qian1
1State Key Laboratory of Advanced Papermaking and Paper-based Materials, South China University of Technology, Guangzhou, 510640, China.
International Journal of Biological Macromolecules
|January 10, 2026
Summary
Accurate lignin quantification is vital for biomass processing. This study shows fused spectra with variable selection methods, particularly MC-UVE, significantly improve lignin prediction accuracy over single spectra.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Biomass Science
Background:
- Accurate lignin quantification is essential for biomass processing and value-added applications.
- Traditional wet chemical methods for lignin analysis are time-consuming and resource-intensive.
- Developing rapid, non-destructive methods for lignin determination is a key research objective.
Purpose of the Study:
- To systematically investigate the efficacy of various variable selection methods (SCARS, CARS, MC-UVE, iMWPLS) for predicting lignin content.
- To evaluate the performance of fused spectra (Raman and ATR-FTIR) compared to single-spectrum modeling.
- To establish a novel technical pathway for rapid, quantitative lignin detection.
Main Methods:
- Utilized Raman and Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy.
- Applied variable selection methods including Sequential Competitive Adaptive Reweighted Sampling (SCARS), Competitive Adaptive Reweighted Sampling (CARS), Monte Carlo Uninformative Variable Elimination (MC-UVE), and Iterative Main Wavelengths Partial Least Squares (iMWPLS).
- Compared full-spectrum modeling with fused spectral data and employed selected variable subsets for prediction.
Main Results:
- Full-spectrum Raman modeling showed limited predictive accuracy (Rp2=0.6221).
- Fused spectra improved predictive accuracy (Rp2=0.7585) compared to single spectra, with MC-UVE achieving optimal results (Rp2=0.8657) under fusion conditions.
- MC-UVE on fused spectra demonstrated significant improvements (14.1% increase in Rp2, 18.3% reduction in RMSEP) over full-spectrum modeling.
Conclusions:
- Variable selection methods are critical for enhancing the accuracy of spectral-based lignin quantification.
- Fused spectra, combined with appropriate variable selection, offer a superior approach for rapid and accurate lignin determination.
- This spectral approach reduces reliance on wet chemistry, enabling real-time, non-destructive monitoring and potentially lowering analytical energy consumption and chemical usage.
