Conditional data processing for single-shot spectral analysis by use of laser-induced breakdown spectroscopy.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis.
• Accurate analyte detection in single-shot LIBS relies on robust data processing metrics.
• Distinguishing true signals from spectral noise is crucial for reliable results.

Purpose of the Study:

• To evaluate and compare peak-to-base ratio and signal-to-noise ratio metrics for analyte detection in single-shot LIBS.
• To assess the impact of conditional data processing thresholds on detection accuracy.
• To determine the optimal metric for minimizing false positives in silica microsphere analysis.

Main Methods:

• Single-shot laser-induced breakdown spectra were generated from silica microspheres.
• Analyte detection focused on the 288.1-nm Si I emission line.
• Spectral noise was characterized using both the Si emission line and continuum regions.
• Conditional processing thresholds were varied to evaluate false hit probabilities.

Main Results:

• Increasing detection thresholds reduced both true particle hits and false positives (spectral noise).
• The signal-to-noise ratio consistently outperformed the peak-to-base ratio across all tested thresholds.
• Signal-to-noise ratio demonstrated greater robustness in single-shot analyte detection.

Conclusions:

• Signal-to-noise ratio is a more reliable metric than peak-to-base ratio for single-shot LIBS analyte detection.
• Optimized data processing using SNR can improve the accuracy of LIBS analysis, particularly in noisy spectral environments.
• This finding has implications for real-time elemental analysis applications using LIBS.