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Related Concept Videos

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...
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Updated: Apr 12, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Raman analysis of black carbon using artificial neural networks for emission source classification.

L Drudi1, M Giardino2, R Bellopede1

  • 1Department of Engineering for Environment, Land and Infrastructure (DIATI), Politecnico di Torino, c.so Duca Degli Abruzzi 24, 10129, Torino, Italy.

Environmental Pollution (Barking, Essex : 1987)
|April 10, 2026
PubMed
Summary

Black Carbon (BC) identification using Raman spectroscopy is now more accurate. This method effectively distinguishes BC sources like biomass and fossil fuels, aiding pollution control strategies.

Keywords:
Artificial neural networkMultilayer perceptronRaman spectroscopySource apportionment

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Black Carbon (BC) is a key air pollutant impacting climate and health.
  • Accurate BC source apportionment is crucial for effective mitigation strategies.
  • Current methods struggle to definitively identify specific combustion sources.

Purpose of the Study:

  • To evaluate Raman spectroscopy for Black Carbon (BC) source apportionment.
  • To differentiate BC originating from gasoline, diesel, and biomass combustion.
  • To develop a reliable method for identifying BC emission sources.

Main Methods:

  • Raman spectroscopy was used to analyze BC samples.
  • A multilayer perceptron (MLP) classifier was trained on spectral data.
  • The MLP model was tested for accuracy in identifying BC sources.

Main Results:

  • The MLP classifier achieved 96.9% accuracy in identifying BC sources.
  • Raman spectroscopy successfully distinguished between fossil fuel and biomass combustion.
  • Analysis of real-world samples showed varying contributions from different sources.

Conclusions:

  • Raman spectroscopy is a reliable technique for BC source apportionment.
  • This method can support the development of routine air pollution monitoring.
  • Accurate source identification enables targeted pollution reduction efforts.