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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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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...
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Raman Spectroscopy: Overview01:20

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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.
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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

4.5K
When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
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NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

6.2K
Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range.
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

7.3K
Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
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Generative Adversarial Network-driven high-resolution Raman spectral generation for accurate molecular feature

Vikas Yadav1, Abhay Kumar Tiwari2, Soumik Siddhanta1

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A Generative Adversarial Network (GAN) enhances portable Raman spectroscopy by generating high-resolution spectra and reducing noise. This improves compound classification and spectral barcoding for pharmaceutical identification.

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Area of Science:

  • Spectroscopy and Photonics
  • Machine Learning and Artificial Intelligence

Background:

  • Raman spectroscopy offers insights into material composition and structure.
  • Portable spectrometers are desirable but often suffer from low resolution and high noise.
  • Effective spectral analysis and compound classification are crucial in various fields.

Purpose of the Study:

  • To integrate a Generative Adversarial Network (GAN) with a portable hand-held spectrometer.
  • To generate high-resolution Raman spectra and reduce background noise.
  • To enable concurrent spectral analysis and compound classification using portable devices.

Main Methods:

  • Development and application of a GAN-based model for Raman spectral data.
  • Utilizing a portable hand-held spectrometer for data acquisition.
  • Training an Artificial Neural Network (ANN) model for classification and spectral barcoding.

Main Results:

  • The GAN model successfully generated high-resolution Raman spectra.
  • Significant reduction in spectral noise was achieved by the GAN model.
  • The integrated system demonstrated accurate classification of organic and pharmaceutical molecules.

Conclusions:

  • The synergy of GANs and portable Raman spectroscopy enables high-quality spectral analysis.
  • This approach facilitates accurate compound identification and spectral barcoding.
  • The integrated system offers a cost-effective solution for real-time monitoring and automated decision-making.