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Related Concept Videos

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy03:49

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During vacuum induction melting, laser-induced breakdown spectroscopy is used to perform real-time quantitative analysis of the main-ingredient elements of a molten...
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Laser-induced breakdown spectroscopy performed on thin organ and tumor tissue successfully detected natural elements and artificially injected gadolinium (Gd), issued from Gd-based nanoparticles. Images of chemical elements reached a resolution of 100 μm and quantitative sub-mM sensitivity. The compatibility of the setup with standard optical microscopy emphasizes its potential to provide multiple images of a same biological...
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LIBS detection capabilities on soil simulants were tested using a range of pulse energies and timing parameters. Calibration curves were used to determine detection limits and sensitivities for different parameters. Generally, the results showed that there was not a significant reduction in detection capabilities using lower pulse energies and non-gated...
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Measurement and Analysis of Atomic Hydrogen and Diatomic Molecular AlO, C2, CN, and TiO Spectra Following Laser-induced Optical Breakdown09:40

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Related Experiment Video

Updated: Jan 20, 2026

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
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A quantitative analysis method assisted by image features in laser-induced breakdown spectroscopy.

Jiujiang Yan1, Zhongqi Hao1, Ran Zhou1

  • 1Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China.

Analytica Chimica Acta
|September 2, 2019
PubMed
Summary

A new image quantitative analysis (IQA) method significantly improves laser-induced breakdown spectroscopy (LIBS) accuracy for high alloy steel analysis. This technique enhances determination precision for elements like nickel, chromium, and silicon.

Keywords:
Image featuresImage quantitative analysisLaser-induced breakdown spectroscopyPartial least squares regressionQuantitative analytical performance

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Last Updated: Jan 20, 2026

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Laser-induced breakdown spectroscopy (LIBS) faces challenges in accurately determining alloying elements in high alloy steels due to significant matrix effects.
  • Conventional spectrum quantitative analysis (SQA) methods exhibit limitations in precision and high error rates for complex steel matrices.

Purpose of the Study:

  • To develop and validate an image quantitative analysis (IQA) method for enhanced elemental determination in high alloy steels using LIBS.
  • To overcome the matrix effect limitations inherent in traditional LIBS quantitative analysis.

Main Methods:

  • An image quantitative analysis (IQA) method was proposed, integrating image processing and computer vision technologies.
  • The IQA method was applied to determine nickel (Ni) in 17 stainless steel samples.
  • Performance was evaluated by comparing with conventional spectrum quantitative analysis (SQA), using metrics like coefficient of determination (R²), average relative error of cross-validation (ARECV), and root mean squared error of cross-validation (RMSECV).

Main Results:

  • The IQA method achieved a coefficient of determination (R²) of 0.9996, a substantial improvement from the SQA method's 0.9833.
  • Average relative error of cross-validation (ARECV) decreased from 56.80% to 15.93%, and root mean squared error of cross-validation (RMSECV) decreased from 1.0818 wt% to 0.9866 wt%.
  • The IQA method demonstrated generalization capabilities by accurately determining chromium (Cr) and silicon (Si).

Conclusions:

  • The proposed image quantitative analysis (IQA) method effectively enhances the quantitative performance of laser-induced breakdown spectroscopy (LIBS) for high alloy steel analysis.
  • This approach offers a robust solution to mitigate matrix effects, leading to significantly improved accuracy and reduced errors.
  • The combination of image processing and computer vision presents a promising avenue for advancing LIBS applications in materials analysis.