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Updated: Apr 12, 2026

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Measurement and Analysis of Atomic Hydrogen and Diatomic Molecular AlO, C2, CN, and TiO Spectra Following Laser-induced Optical Breakdown
Published on: February 14, 2014
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[The research on separating and extracting overlapping spectral feature lines in LIBS using damped least squares
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|May 15, 2015
Summary
Damped least squares method effectively separates overlapping spectral lines in laser induced breakdown spectroscopy. This technique improves chromium concentration analysis in fly ash compared to traditional least squares methods.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Materials Science
Context:
- Laser induced breakdown spectroscopy (LIBS) is a rapid, in-situ elemental analysis technique.
- Accurate spectral feature extraction is crucial for LIBS data interpretation and quantitative analysis.
- Overlapping spectral lines pose a significant challenge in LIBS analysis.
Purpose:
- To evaluate the effectiveness of the damped least squares method for separating and extracting overlapped spectral feature lines in LIBS.
- To compare the performance of damped least squares method against the standard least squares method for spectral analysis.
- To determine the optimal parameters for spectral line fitting in LIBS.
Summary:
- The damped least squares method, incorporating dynamic step adjustment, was applied to separate overlapped spectral lines of chromium (Cr I) and iron (Fe I) in fly ash samples.
- Intensity values derived from damped least squares fitting showed a better linear correlation with chromium concentrations than those from the standard least squares method.
- Calibration curves demonstrated the superior performance of the damped least squares method in quantitative analysis.
Impact:
- The damped least squares method provides a stable and reliable approach for spectral feature line separation, fitting, and extraction in LIBS.
- This improved spectral analysis leads to more accurate elemental concentration measurements.
- The findings enhance the applicability of LIBS for complex sample analysis in various fields.
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