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    This study introduces an automated method using Genetic Algorithms to select optimal spectral lines for quantitative analysis in Laser-Induced Breakdown Spectroscopy (LIBS). This approach improves accuracy and efficiency in determining elements in low alloy steels.

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    Area of Science:

    • Analytical Chemistry
    • Spectroscopy
    • Materials Science

    Background:

    • Quantitative analysis in Laser-Induced Breakdown Spectroscopy (LIBS) relies on selecting appropriate characteristic spectral lines.
    • Manual selection of these lines is inefficient and may not yield optimal results for the internal standard method.
    • Accurate selection of analytical and reference lines is crucial for reliable elemental quantification.

    Purpose of the Study:

    • To develop and validate an automated method for selecting optimal analytical and reference spectral lines for LIBS quantitative analysis using the internal standard method.
    • To improve the efficiency and accuracy of elemental determination in low alloy steels.
    • To demonstrate the effectiveness of a Genetic Algorithm-based approach for spectral line selection.

    Main Methods:

    • A novel method based on Genetic Algorithm was developed for automatic selection of analytical and reference lines from spectral data.
    • The method was applied to LIBS spectra of Mn, Ni, Cr, Si, and Fe in low alloy steels.
    • Selected lines were used for quantitative analysis via the internal standard method.

    Main Results:

    • The Genetic Algorithm successfully identified optimal analytical and reference lines for Mn, Si, Cr, and Ni against Fe as the internal standard.
    • Specific optimal line pairs were identified, e.g., Mn (403.3068 nm) / Fe (368.7457 nm).
    • Quantitative analysis using the selected lines yielded accurate results, confirming the method's efficacy.

    Conclusions:

    • The proposed automated method effectively selects optimal characteristic lines for LIBS quantitative analysis.
    • The Genetic Algorithm approach provides a more efficient and reliable alternative to manual line selection.
    • This method ensures the best possible quantitative results when employing the internal standard method in LIBS analysis.