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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

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...
Raman Spectroscopy: Overview01:20

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 the...
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
Applications Of NMR In Biology01:25

Applications Of NMR In Biology

Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
The...
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

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 the...

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Related Experiment Video

Updated: Jun 18, 2026

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

Mathematical and machine learning-assisted modelling of Raman spectroscopy for biomedical applications.

Jorge Servert Lerdo de Tejada1, Jan Vališ2, Lukáš Hrubčík2

  • 1School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Rd., Manchester, M13 9PL, UK.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|June 16, 2026
PubMed
Summary

Mathematical and machine learning methods enhance Raman spectroscopy for biomedical applications. This review critically evaluates spectral generation techniques to overcome challenges in clinical use, aiming for improved safety and efficiency.

Keywords:
Artificial intelligenceBiomedicineData augmentationGenerative modelsRaman spectroscopySpectral simulation

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An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
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An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

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Last Updated: Jun 18, 2026

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Computational Science

Background:

  • Raman spectroscopy is a sensitive, non-destructive technique for biochemical analysis.
  • Applications include surgical assistance and cancer diagnostics, especially with machine learning.
  • Challenges include data complexity, sample variability, and imbalanced datasets, hindering clinical translation.

Purpose of the Study:

  • To critically review mathematical and machine learning-assisted spectral generation methods for Raman spectroscopy in biomedicine.
  • To evaluate techniques for enhancing spectral data quality and applicability.
  • To identify challenges and future directions for clinical translation.

Main Methods:

  • Review of bottom-up quantum mechanics simulations (DFT, TD-DFT).
  • Analysis of AI-assisted spectral generation techniques (GANs, Auto-encoders).
  • Evaluation of existing mathematical approaches for signal optimization and safety analysis.

Main Results:

  • Various spectral generation techniques offer distinct advantages and limitations.
  • Mathematical approaches can aid signal optimization, safety analysis, and probe design.
  • Transitioning from in vitro to in vivo applications presents significant hurdles.

Conclusions:

  • Spectral generation methods can improve Raman spectroscopy's clinical potential.
  • Accessible mathematical approaches can guide instrumentation and software development.
  • Further research is needed to address open challenges for robust in vivo applications.