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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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Raman Spectroscopy Instrumentation: Overview01:26

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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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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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Related Experiment Video

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Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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Investigating microcrystalline cellulose crystallinity using Raman spectroscopy.

Ana Luiza P Queiroz1, Brian M Kerins1, Jayprakash Yadav2

  • 1SSPC Pharmaceutical Research Centre, School of Pharmacy, University College Cork, Cork, Ireland.

Cellulose (London, England)
|November 1, 2021
PubMed
Summary

This study developed Raman spectroscopy models to accurately measure microcrystalline cellulose (MCC) crystallinity. These models enable rapid, reliable assessment of MCC crystallinity, crucial for consistent downstream processing.

Keywords:
CrystallinityMicrocrystalline cellulosePartial least square regressionR ShinyRaman spectroscopy

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Characterization of Nanocrystal Size Distribution using Raman Spectroscopy with a Multi-particle Phonon Confinement Model
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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Analytical Chemistry

Background:

  • Microcrystalline cellulose (MCC) exhibits variable crystallinity influenced by raw material and manufacturing.
  • This variability can lead to inconsistencies in downstream pharmaceutical processes.
  • Accurate and rapid assessment of MCC crystallinity is essential for quality control.

Purpose of the Study:

  • To develop and validate models for determining the crystallinity index (%CI) of MCC using Raman spectroscopy.
  • To compare the effectiveness of different Raman probe sizes (100 µm and 6 mm) for crystallinity assessment.
  • To establish a rapid method for MCC crystallinity analysis, reducing processing time.

Main Methods:

  • Raman spectra were acquired from 30 commercial MCC batches using 100 µm (MR probe) and 6 mm (PhAT probe) spot sizes.
  • Principal Component Analysis (PCA) was used to differentiate spectra obtained from different probes.
  • Partial Least Squares (PLS) regression models were developed to predict %CI from Raman spectra, alongside a univariate model adjusted for each probe.

Main Results:

  • PCA effectively separated spectra acquired with the MR and PhAT probes.
  • Both univariate and PLS models demonstrated adequate predictive power for MCC %CI.
  • The PLS model significantly reduced analysis time by eliminating the need for spectral deconvolution.
  • A general reference amorphous spectrum was proposed for each instrument to improve model accuracy.

Conclusions:

  • Raman spectroscopy, particularly with PLS modeling, provides a robust and efficient method for quantifying MCC crystallinity.
  • The developed models and web application facilitate rapid, on-site assessment of MCC quality.
  • This approach helps mitigate downstream process variability associated with MCC crystallinity differences.