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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Related Experiment Video

Updated: May 7, 2026

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part I: Lignin
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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
PubMed
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.

Keywords:
Infrared spectrumLife cycle assessmentLignin contentQuantitative analysis

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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.