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Energetic materials identification by laser-induced breakdown spectroscopy combined with artificial neural network
Applied Optics
|April 22, 2017
Summary
This study combines laser-induced breakdown spectroscopy (LIBS) with artificial neural networks (ANNs) for identifying energetic materials. The ANN3 algorithm, using PCA scores in argon, achieved the best identification accuracy.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Machine Learning
Background:
- Accurate identification of energetic materials is crucial for safety and security.
- Traditional methods may lack speed or specificity.
- Laser-induced breakdown spectroscopy (LIBS) offers rapid elemental analysis.
Purpose of the Study:
- To develop and validate a novel method for identifying energetic materials.
- To combine LIBS with artificial neural network (ANN) analysis for enhanced material discrimination.
- To compare the performance of different ANN configurations and ambient conditions.
Main Methods:
- Samples of energetic materials (TNT, RDX, black powder, propellant) and reference materials (Al, Cu, inconel, graphite) were analyzed using LIBS.
- LIBS spectra were acquired in both air and argon atmospheres.
- Three artificial neural network (ANN) algorithms were trained: ANN1 (full spectra in air), ANN2 (PCA scores in air), and ANN3 (PCA scores in argon).
- The performance of each ANN was evaluated based on identification accuracy and error rates.
- Validation was performed using Al/RDX standard samples.
Main Results:
- The developed combined LIBS-ANN method successfully identified energetic materials.
- ANN algorithms utilizing Principle Component Analysis (PCA) scores (ANN2 and ANN3) showed significantly lower error rates compared to ANN1.
- ANN3, which used PCA scores from spectra acquired in argon, demonstrated the highest identification and discrimination accuracy.
- Validation with Al/RDX samples confirmed the method's reliability.
Conclusions:
- The combination of LIBS and ANN analysis provides a powerful tool for the rapid and accurate identification of energetic materials.
- Utilizing PCA for spectral data reduction and performing LIBS analysis in an argon atmosphere enhances identification performance.
- This approach holds significant potential for applications in security, forensics, and industrial quality control.
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