Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Materials Science

Background:

• Laser-induced breakdown spectroscopy (LIBS) offers rapid, minimal-preparation multi-elemental analysis.
• Traditional LIBS uses fixed parameters, limiting spectral differences for similar samples.
• This similarity reduces LIBS sensitivity, accuracy, and robustness in qualitative analysis.

Purpose of the Study:

• To develop a novel method for increasing spectral differences between similar samples using LIBS.
• To enhance the accuracy and robustness of qualitative analysis for challenging sample sets.
• To validate the proposed method on real-world samples like coal.

Main Methods:

• Proposed a 'multiple-setting spectra' approach, acquiring spectra with varied experimental parameters (laser energy, delay time) for each sample.
• Applied principal component analysis (PCA) and Davies-Bouldin index (DBI) for spectral data processing and analysis.
• Tested the method on 11 similar coal samples to evaluate its effectiveness.

Main Results:

• The multiple-setting spectra method significantly improved sample discrimination accuracy from 81.8% to 96.4%.
• Enhanced spectral fingerprint information was generated for each sample.
• The method demonstrated effectiveness in distinguishing between closely related samples.

Conclusions:

• The multiple-setting spectra technique substantially enhances LIBS capability for analyzing similar samples.
• This approach maintains the inherent efficiency and cost-effectiveness of LIBS.
• The findings suggest a valuable improvement for LIBS applications requiring high discrimination accuracy.