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Discrimination of organic solid materials by LIBS using methods of correlation and normalized coordinates.
R J Lasheras1, C Bello-Gálvez, E M Rodríguez-Celis
1Laser Laboratory and Environment, Department of Analytical Chemistry, Faculty of Sciences, University of Zaragoza, Pedro Cerbuna #12, 50009 Zaragoza, Spain.
Journal of Hazardous Materials
|June 25, 2011
Summary
Linear and rank correlation methods offer superior identification of organic materials using laser-induced breakdown spectroscopy (LIBS) compared to normalized methods. These correlation techniques enhance material discrimination with high confidence.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Materials Science
Background:
- Laser-Induced Breakdown Spectroscopy (LIBS) is a powerful technique for elemental analysis.
- Distinguishing between organic materials with similar chemical compositions can be challenging.
- Statistical methods are crucial for interpreting complex LIBS data.
Purpose of the Study:
- To evaluate the effectiveness of linear correlation, rank correlation, and normalized coordinates (MNC) methods for identifying organic solids using LIBS.
- To assess the influence of instrumental parameters on LIBS signal quality for carbon and hydrogen.
- To determine the probability of correct material identification using these statistical approaches.
Main Methods:
- Application of linear correlation, rank correlation, and normalized coordinates (MNC) methods.
- Utilizing an Echelle spectrometer with an intensified charge-coupled device (ICCD).
- Optimization of instrumental parameters: laser pulse energy, delay time, and integration time.
Main Results:
- Correlation methods demonstrated superior identification and discrimination capabilities compared to MNC.
- The signal-to-noise ratio of carbon and hydrogen emission lines was analyzed concerning instrumental parameters.
- A method for estimating the probability of correct identification was established.
Conclusions:
- Linear and rank correlation methods provide more reliable identification of organic materials with similar compositions via LIBS.
- The study validates the utility of statistical analysis in enhancing LIBS material discrimination.
- Instrumental parameter optimization is key to improving the accuracy of LIBS-based material identification.
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