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Accuracy improvement of quantitative analysis in laser-induced breakdown spectroscopy using modified wavelet

X H Zou, L B Guo, M Shen

    Optics Express
    |June 13, 2014
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    Summary

    A new wavelet transform algorithm enhances background removal for laser-induced breakdown spectroscopy (LIBS). This improved spectrum correction significantly boosts the accuracy of quantitative analysis for elements in low alloy steel.

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    Area of Science:

    • Analytical Chemistry
    • Spectroscopy
    • Signal Processing

    Background:

    • Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful technique for elemental analysis.
    • Background noise in LIBS spectra can significantly hinder accurate quantitative analysis.
    • Effective background removal is crucial for improving LIBS data reliability.

    Purpose of the Study:

    • To develop and validate a modified wavelet transform algorithm for background removal in LIBS.
    • To optimize the algorithm's parameters using calibration metrics.
    • To assess the impact of the algorithm on the accuracy of quantitative elemental analysis in low alloy steel.

    Main Methods:

    • A modified background removal algorithm based on wavelet transform was developed.
    • Optimization of wavelet function type, decomposition level, and scaling factor using Root-Mean-Square Error of Calibration (RMSEC).
    • Application of the optimized algorithm for spectrum correction and quantitative analysis of Cr, V, Cu, and Mn in low alloy steel.

    Main Results:

    • The modified wavelet transform algorithm effectively removed background noise from LIBS spectra.
    • Optimization using RMSEC identified optimal parameters for the algorithm.
    • Significant improvements in accuracy were observed for quantitative analysis of Cr, V, Cu, and Mn, validated by RMSECV and ARE.

    Conclusions:

    • The developed modified wavelet transform algorithm is an effective pretreatment method for LIBS.
    • This approach significantly enhances the accuracy of quantitative elemental analysis in complex matrices like low alloy steel.
    • The optimized algorithm offers a robust solution for improving LIBS data quality.