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Updated: May 23, 2026

Quantitative Analysis of Vacuum Induction Melting by Laser-induced Breakdown Spectroscopy
Published on: June 10, 2019
[Study on the automatic recognition method of elemental spectra in laser induced breakdown spetroscopy]
Li-tuo Liu1, Jian-guo Liu, Nan-jing Zhao
1Key Laboratory of Environment Optics & Technology, Institute of Anhui Optics Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China. ltuoliu@aiofm.ac.cn
This study introduces an automated method for elemental spectra recognition in laser-induced breakdown spectroscopy (LIBS) by comparing simulated and experimental spectral data. The technique successfully identifies soil spectral data, demonstrating its practical feasibility.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Atomic Emission Spectroscopy
Context:
- Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful technique for elemental analysis.
- Accurate spectral data interpretation is crucial for reliable elemental identification.
- Simulating spectral lines and comparing them with experimental data aids in method validation.
Purpose:
- To develop and validate an automated method for elemental spectra recognition using LIBS.
- To compare simulated emission lines from the NIST spectral database with experimental spectral data.
- To assess the similarity between simulated and experimental spectra using a similarity measure.
Summary:
- Emission lines from the NIST spectral database were simulated based on spectral line broadening mechanisms in LIBS.
- A similarity measure was employed to compare simulated spectra with experimental data.
- An automated recognition method for elemental spectra, specifically soil samples (340-345 nm), was developed using proportional coefficients and nonlinear least squares.
Impact:
- The developed method offers a feasible and advantageous approach for automated elemental spectra recognition in LIBS.
- This research contributes to improving the accuracy and efficiency of elemental analysis in complex samples like soil.
- The findings validate the use of spectral simulation and similarity measures for LIBS data analysis.
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