Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

David D. Pokrajac, Delaware State University
Poopalasingam Sivakumar, Southern Illinois University Carbondale
Yuriy Markushin, Delaware State University
Daniela Milovic, University of Niš
Gary Holness, Delaware State University
Jinjie Liu, Delaware State University
Noureddine Melikechi, University of Massachusetts Lowell
Mukti Rana, Delaware State University

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of “dark signal” and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.