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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Raman Spectroscopy: Overview01:20

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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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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
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Updated: Sep 17, 2025

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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Fusing Raman Spectra with Fiber Metrics in Machine Learning Models That Predict the Physical-Mechanical Properties of

Zahra Poursorkh1, Hooman Tavakolizadeh1, Ashton Christy1

  • 1Department of Chemistry, University of British Columbia, Vancouver, BC V6T 1Z1, Canada.

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Summary

This study introduces a new method using Raman spectroscopy and PulpEye data fusion with machine learning to predict paper properties in real-time. This approach reduces reliance on manual testing for pulp quality control.

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Area of Science:

  • Pulp and Paper Science
  • Spectroscopy
  • Machine Learning

Background:

  • Conventional pulp quality control relies on time-consuming manual testing, limiting real-time process management.
  • Current methods demand significant personnel and material resources.
  • There is a need for faster, more efficient methods to monitor pulp properties.

Purpose of the Study:

  • To develop an alternative approach for real-time pulp quality control.
  • To integrate data from Raman spectroscopy and an online physical analysis system (PulpEye).
  • To utilize machine learning for predicting paper properties from pulp attributes.

Main Methods:

  • Data fusion of Raman spectroscopy and PulpEye measurements.
  • Development of machine learning models (XGBoost, PLSR) to predict 20 paper properties.
  • Training models using data from over 500 production pulp samples and conventional quality control tests.

Main Results:

  • Machine learning models, particularly XGBoost, accurately predict paper properties.
  • Prediction errors are comparable to traditional wet-lab measurements.
  • The integrated approach provides instantaneous input for analysis.

Conclusions:

  • The developed method offers a promising solution for real-time process control in the pulp and paper industry.
  • This data-driven approach reduces reliance on manual testing.
  • It facilitates a deeper understanding of factors influencing cellulosic material properties.