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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

292
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...
292
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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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...
298

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Updated: May 28, 2025

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy
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Low-Cost Raman Spectroscopy Setup Combined with a Machine Learning Model.

Catarina Domingos1, Alessandro Fantoni1,2, Miguel Fernandes1,2

  • 1Department of Electronics, Telecommunication and Computers, Lisbon School of Engineering (ISEL), Polytechnic University of Lisbon (IPL), Rua Conselheiro Emídio Navarro, n°1, 1959-007 Lisbon, Portugal.

Sensors (Basel, Switzerland)
|February 13, 2025
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Summary
This summary is machine-generated.

Researchers developed a portable, low-cost Raman spectroscopy system for disease diagnosis. This affordable system analyzes urine samples, offering potential for point-of-care applications and early disease risk assessment.

Keywords:
Raman spectroscopydiagnosisinstrumentationkidney diseasepoint of caresensor

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Area of Science:

  • Analytical Chemistry
  • Biomedical Engineering
  • Spectroscopy

Background:

  • Kidney disease diagnosis is challenging due to unreliable biomarkers and complex lab tests.
  • Raman spectroscopy offers potential for analyzing biofluids like urine but faces accessibility and complexity issues.
  • Current Raman spectral analysis is intensive and time-consuming.

Purpose of the Study:

  • To develop a portable, simplified, and low-cost Raman spectroscopy system for complex liquid sample analysis.
  • To optimize the system using the OpenRAMAN project's methodology.
  • To validate the system's performance for urine sample analysis and spectral classification.

Main Methods:

  • Developed a portable, low-cost Raman system optimized via laser temperature and software acquisition parameter adjustments.
  • Validated the system by acquiring Raman spectra from five urine samples.
  • Designed and trained a neural network using methanol and ethanol solutions, optimizing hyperparameters for accuracy.

Main Results:

  • The system demonstrated consistency and sensitivity to variations in urine sample composition.
  • The trained neural network achieved 99.19% accuracy and 99.21% precision in classifying simple Raman spectra.
  • The system's development and training process was efficient, with a 3-minute training time for the neural network.

Conclusions:

  • The developed Raman system is affordable and portable, suitable for point-of-care applications.
  • This technology simplifies disease risk assessment outside clinical settings.
  • Further validation and integration with advanced features are needed for comprehensive biomarker analysis.