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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.
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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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
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Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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Variational Mode Decomposition for Raman Spectral Denoising.

Xihui Bian1,2, Zitong Shi1, Yingjie Shao1

  • 1State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China.

Molecules (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

Variational mode decomposition (VMD) offers an improved method for denoising Raman spectra, outperforming traditional techniques like empirical mode decomposition (EMD) and discrete wavelet transformation (DWT) in preserving spectral information and signal-to-noise ratio.

Keywords:
Raman spectrumdenoisingempirical mode decompositionmode mixingvariational mode decomposition

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Area of Science:

  • Chemistry
  • Spectroscopy
  • Signal Processing

Background:

  • Raman spectroscopy is a valuable chemical analysis tool, but noise often hinders accurate interpretation.
  • Effective denoising is crucial for reliable Raman spectral analysis.

Purpose of the Study:

  • To introduce and evaluate a novel spectral denoising method using variational mode decomposition (VMD) for Raman spectra.
  • To compare VMD's performance against empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT).

Main Methods:

  • The proposed method decomposes Raman spectra into intrinsic modes using VMD.
  • High-frequency noise modes are identified and removed.
  • The remaining modes are reconstructed to obtain a denoised spectrum.
  • Performance is evaluated using artificial signals and inorganic material spectra (MnCo ISAs/CN, Fe-NCNT).

Main Results:

  • VMD effectively reduces mode mixing and endpoint effects compared to EMD.
  • VMD-based denoising yields superior visualization and signal-to-noise ratio (SNR) over EMD, SG, and DWT.
  • While some information loss occurs in all methods, VMD minimizes data loss for small, sharp peaks.

Conclusions:

  • Variational mode decomposition presents a promising alternative for denoising Raman spectra.
  • VMD offers advantages in noise reduction while preserving essential spectral features.
  • The method demonstrates significant potential for enhancing Raman spectral analysis.