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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

325
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...
325
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

360
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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¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

1.4K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
1.4K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

1.3K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
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Related Experiment Video

Updated: Jun 24, 2025

Quantifying Mixing using Magnetic Resonance Imaging
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RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components.

Onur Can Koyun1, Reyhan Kevser Keser1, Safa Onur Şahin2

  • 1Signal Processing for Computational Intelligence Research Group (SP4CING), Informatics Institute, Istanbul Technical University, 34469 Istanbul, Turkey.

ACS Omega
|June 10, 2024
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Summary

This study introduces RamanFormer, a transformer model that precisely identifies and quantifies components in chemical mixtures using Raman spectroscopy. It significantly improves accuracy over traditional methods, enhancing material analysis reliability.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Raman spectroscopy offers unique molecular fingerprints for material identification.
  • Analyzing complex mixtures is challenging due to overlapping spectral data.
  • Existing methods struggle with distinguishing components with similar spectral features.

Purpose of the Study:

  • To develop an advanced model, RamanFormer, for enhanced Raman spectroscopy data analysis.
  • To improve the precision of component identification and quantification in chemical mixtures.
  • To overcome limitations of traditional methods in analyzing complex Raman spectra.

Main Methods:

  • A transformer-based deep learning model, RamanFormer, was developed.
  • The model utilizes self-attention mechanisms to process sequential spectral data.
  • RamanFormer was trained and validated on binary and ternary chemical mixtures.

Main Results:

  • RamanFormer achieved high precision in component identification and quantification.
  • The model demonstrated a mean absolute error of 1.4% and root mean squared error of 1.6%.
  • Performance was robust across various noise levels (up to 10 dB SNR).

Conclusions:

  • RamanFormer significantly outperforms traditional methods like least squares, MLP, VGG11, and ResNet50.
  • The model enhances the reliability of material identification in complex mixtures.
  • This advancement broadens Raman spectroscopy applications in material science, forensics, and diagnostics.