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Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
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An image features assisted line selection method in laser-induced breakdown spectroscopy.

Jiujiang Yan1, Shuhan Li1, Kun Liu1

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

Analytica Chimica Acta
|April 22, 2020
PubMed
Summary

A new method using image features (IFALS) significantly improved laser-induced breakdown spectroscopy (LIBS) rock classification accuracy to 98.54%. This approach also drastically reduced analysis time, enhancing overall LIBS efficiency.

Keywords:
Analytical line selectionImage featuresLaser-induced breakdown spectroscopyLinear discrimination analysisMetamorphic rock

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Vision

Background:

  • Analytical lines are critical for laser-induced breakdown spectroscopy (LIBS) performance.
  • Improving LIBS classification accuracy and efficiency is an ongoing challenge.

Purpose of the Study:

  • To develop an image features assisted line selection (IFALS) method for enhanced LIBS analysis.
  • To evaluate the effectiveness of IFALS in metamorphic rock classification.

Main Methods:

  • Proposed IFALS method utilizing spectral morphology and Harris corners.
  • Conducted classification experiments on 24 metamorphic rock samples using linear discriminant analysis (LDA).
  • Compared IFALS-LDA with conventional manual line selection (MLS-LDA) and automatic spectral peaks identification (ASPI-LDA).

Main Results:

  • IFALS-LDA achieved a classification accuracy of 98.54%, an improvement from 94.38% with MLS-LDA.
  • The IFALS method reduced classification time from 2768.38 s to 4.36 s.
  • IFALS-LDA demonstrated a significantly faster convergence rate than ASPI-LDA.

Conclusions:

  • LIBS analysis performance is enhanced by integrating machine vision image features.
  • The IFALS method offers a more accurate, efficient, and faster approach for LIBS spectral line selection.
  • This study highlights the potential of image-assisted LIBS for advanced material analysis.