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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

486
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...
486
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

499
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...
499

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Related Experiment Video

Updated: Aug 9, 2025

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
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Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from

Guillaume Sheehy1,2, Fabien Picot1,2, Frédérick Dallaire1,2

  • 1Polytechnique Montréal, Department of Engineering Physics, Montreal, Quebec, Canada.

Journal of Biomedical Optics
|February 24, 2023
PubMed
Summary
This summary is machine-generated.

Standardized bio-Raman spectroscopy data processing is crucial for comparing results across studies. An open-source software package with a novel baseline removal technique ensures data compatibility and enables new clinical applications.

Keywords:
Raman spectroscopyfluorescencemachine learningopen-sourced softwareopticstissue optics

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Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Data Science

Background:

  • Standardized data processing is essential for reproducible and comparable results in bio-Raman spectroscopy.
  • Existing methods struggle with complex baselines and data variability across different systems.
  • Inter-system data compatibility is a significant challenge in translating Raman spectroscopy to clinical applications.

Purpose of the Study:

  • To develop and validate an open-sourced data processing software package for bio-Raman spectroscopy.
  • To introduce a novel morphological baseline removal technique (BubbleFill) for improved adaptability.
  • To ensure inter-systems data compatibility for clinical translation.

Main Methods:

  • Development of an open-sourced software package implementing a complete data processing workflow for Raman spectroscopy.
  • Inclusion of a novel morphological baseline removal technique, BubbleFill.
  • Incorporation of a versatile tool for simulating spectroscopic data with controlled noise, baseline, and signal-to-background ratios.
  • Validation of the BubbleFill technique against standard algorithms (iModPoly, MorphBR) using simulated data.
  • Validation of the overall data processing workflow on four independent in-human datasets.

Main Results:

  • The BubbleFill technique demonstrated superior baseline removal performance compared to iModPoly and MorphBR on simulated data.
  • The open-sourced package's data processing workflow achieved inter-systems data compatibility when validated on in-human datasets.
  • Simulated data generation tool allows for robust testing of algorithms under various conditions.

Conclusions:

  • A new, open-sourced spectroscopic data pre-processing package has been developed and validated.
  • The package, featuring the innovative BubbleFill technique, enhances data comparability in bio-Raman spectroscopy.
  • This validated tool is now available to researchers and clinicians, facilitating the development of novel clinical applications using Raman spectroscopy.