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Raman Spectroscopy: Overview

Raman Spectroscopy: Overview

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

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A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

Published on: April 12, 2017

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

Ramie N Ali-Adeeb¹, Phil Shreeves², Xinchen Deng¹

¹Department of Physics, The University of British Columbia - Okanagan campus, Kelowna, BC, Canada.

|December 30, 2022

Summary

Raman spectroscopy accurately differentiates radiation-induced lung toxicity in mice, achieving 91.6% accuracy for fibrotic grading. This method provides a foundation for predictive disease models in radiation toxicology.

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Last Updated: Aug 15, 2025

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Area of Science:

Biomedical Optics
Medical Physics
Toxicology

Background:

Radiation therapy can cause lung toxicity, necessitating accurate methods for assessment.
Early detection and grading of radiation-induced lung injury are crucial for patient management.
Developing predictive models for radiation-induced toxicity requires reliable quantitative biomarkers.

Purpose of the Study:

To develop and validate a Raman spectroscopy-based method for measuring and differentiating radiation-induced toxicity in murine lungs.
To establish a foundation for a predictive disease model for radiation-induced lung injury.
To assess the potential of Raman spectroscopy in classifying fibrotic gradings and pneumonitis.

Main Methods:

Raman spectroscopy was used to collect tissue data from murine lungs.
Group and basis restricted non-negative matrix factorization distinguished tissue from air pockets.
Sparse multinomial logistic regression analyzed tissue spectra for fibrotic grading discrimination, with data split into 70% training and 30% testing sets.

Main Results:

A classification accuracy of 91.6% was achieved on the test set for fibrotic gradings.
Raman measurements demonstrated the ability to detect varying levels of fibrotic disease in murine lungs.
Coarser grading (Low, Medium, High) did not significantly degrade classification performance; preliminary models for pneumonitis were also explored.

Conclusions:

Raman spectroscopy is a highly accurate method for quantifying and differentiating radiation-induced fibrotic lung toxicity in a murine model.
The developed methodology shows promise for establishing a predictive disease model for radiation-induced lung injury.
This technique offers a non-invasive approach for assessing lung tissue changes following radiation exposure.