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Related Concept Videos

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...
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...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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Related Experiment Video

Updated: Jun 5, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Machine Learning-Integrated Raman Spectroscopy Identifies Race-Associated Biochemical Signatures in Prostate Cancer.

Maria Iftesum1, Gyana Ranjan Sahoo2, Elnaz Sheikh1

  • 1Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, Louisiana, USA.

Journal of Biophotonics
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Raman spectroscopy reveals distinct biochemical differences in prostate tumors between Black and White men, offering insights into prostate cancer racial disparities. This label-free technique shows promise for identifying aggressive disease markers.

Keywords:
ICA‐PLSMCR‐ALSRaman spectroscopyprincipal component analysisprostate cancerracial disparityrandom Forest classification

Related Experiment Videos

Last Updated: Jun 5, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

  • Biochemistry
  • Medical Spectroscopy
  • Oncology

Background:

  • Black men face higher prostate cancer incidence and mortality.
  • Biochemical factors contributing to these disparities are not fully understood.

Purpose of the Study:

  • To investigate molecular differences in prostate tissues between Black and White patients using Raman spectroscopy.
  • To identify biochemical markers associated with prostate cancer racial disparities.

Main Methods:

  • Utilized Raman spectroscopy on formalin-fixed, paraffin-embedded (FFPE) prostate tissues.
  • Applied independent component analysis-partial least squares (ICA-PLS) for spectral correction and wavelet denoising.
  • Employed Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for quantifying cellular components.
  • Trained Random Forest (RF) models for cancer versus control tissue classification.

Main Results:

  • MCR-ALS identified elevated protein, collagen, lipid, and nucleic acid signatures in tumors from Black patients.
  • RF models achieved over 90% accuracy, 95% sensitivity, 85% specificity, and an AUC > 0.96 for tissue classification.
  • Biochemical profiles correlated with clinically observed aggressive disease phenotypes.

Conclusions:

  • Raman spectroscopy combined with computational analysis offers a powerful label-free method to study prostate cancer.
  • This approach can probe biochemical drivers underlying racial disparities in prostate cancer.
  • Findings support the potential of spectroscopic methods for diagnosing and understanding prostate cancer aggressiveness.