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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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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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Mass Spectrometry: Complex Analysis01:21

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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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NMR Spectrometers: Resolution and Error Correction01:14

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Updated: Oct 1, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
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Performance Improvement of Handheld Raman Spectrometer for Mixture Components Identification Using Fuzzy Membership

Xin Zhao1, Caizheng Liu1, Ziyan Zhao1

  • 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), 66374Jiangnan University, Wuxi, Jiangsu, China.

Applied Spectroscopy
|March 8, 2022
PubMed
Summary

This study introduces a new method for identifying components in mixtures using handheld Raman spectroscopy. The technique effectively overcomes spectral challenges, improving accuracy for both liquid and powder samples.

Keywords:
Raman spectroscopycomponent identification in mixturefuzzy membership functionsimilarity analysissparse non-negative least squares

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Handheld Raman spectrometers offer convenient, in-situ analysis for mixture component identification.
  • Challenges include significant peak overlapping and spectral distortion in collected spectra, hindering accurate identification.
  • Existing methods struggle with complex mixture spectra obtained from portable devices.

Purpose of the Study:

  • To develop and validate a novel method for accurate mixture component identification using handheld Raman spectroscopy.
  • To address the limitations of spectral overlapping and distortion in field-based Raman analysis.
  • To enhance the reliability of component identification in diverse mixture types.

Main Methods:

  • Utilized wavelet transform and Voight curve fitting to extract key spectral parameters (Raman shift, maximum intensity, FWHM).
  • Employed fuzzy membership functions to calculate similarity between mixture spectra and database substances.
  • Applied sparse non-negative least squares (sNNLS) for fitting candidate spectra to mixture spectra for final determination.

Main Results:

  • Successfully extracted feature parameters from Raman spectral peaks, enabling robust similarity calculations.
  • Preliminary screening of candidate substances based on fuzzy similarity effectively narrowed down possibilities.
  • The sNNLS fitting approach accurately determined components, validated across 190 liquid and 158 powder mixture samples.
  • Achieved good identification accuracy for various mixture compositions and types.

Conclusions:

  • The proposed method effectively overcomes spectral distortion and overlapping issues common in handheld Raman spectroscopy.
  • This novel approach provides an accurate and reliable solution for in-situ mixture component identification.
  • The technique demonstrates significant potential for field applications requiring rapid and precise chemical analysis.