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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...

Raman Spectroscopy Instrumentation: Overview

Raman Spectroscopy Instrumentation: Overview

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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Updated: Sep 20, 2025

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

Differentiation of Healthy Ex Vivo Bovine Tissues Using Raman Spectroscopy and Interpretable Machine Learning.

Soha Yousuf¹, Mohamed Irfan Karukappadath¹, Azhar Zam^1,2

¹Division of Engineering, Laboratory for Advanced Bio-Photonics and Imaging (LAB-π), New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Lasers in Surgery and Medicine

|May 28, 2025

Summary

This summary is machine-generated.

Interpretable machine learning models combined with Raman spectroscopy (RS) accurately differentiate orthopedic tissues. Identifying key Raman biomarkers enhances transparency and trust in RS for surgical guidance.

Keywords:

CNN Raman spectroscopy SVM bovine tissue machine learning matching score spectral feature importance

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Last Updated: Sep 20, 2025

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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

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Published on: September 26, 2019

Area of Science:

Biomedical Engineering
Spectroscopy
Machine Learning

Background:

Raman spectroscopy (RS) shows promise for intraoperative guidance in orthopedic surgery.
Limited algorithm transparency in machine learning (ML) hinders clinician trust.

Purpose of the Study:

Develop interpretable ML models for classifying bovine tissues (bone, marrow, fat, muscle).
Identify key Raman biomarkers to enhance model transparency and clinical trust.

Main Methods:

Collected spectral data from bovine tissues using a portable RS system.
Developed and evaluated 1D-CNN and SVM models for tissue classification.
Generated feature importance maps and matching scores to identify Raman biomarkers.

Main Results:

Identified critical Raman biomarkers: hydroxyapatite, lipids, amino acids, and collagen.
These biomarkers are essential for distinguishing between different bovine tissue types.
Provided insights into the molecular differences between tissues.

Conclusions:

Interpretable ML models integrated with RS accurately differentiate orthopedic tissues.
Biomarker identification enhances transparency, supporting RS as a reliable tool for precision surgery.