Alternative Statistical Methods for Spectral Data Processing: Applications to Laser-Induced Breakdown Spectroscopy of Gaseous and Aerosol Systems

Abstract

A new spectral data processing scheme based on the standard deviation of collected spectra is compared with the traditional ensemble-averaging of laser-induced breakdown spectroscopy (LIBS)-based spectral data for homogenous (i.e., pure gas phase) systems and with a LIBS-based traditional conditional spectral analysis scheme for non-homogenous (e.g., aerosol system) analyte systems under discrete particle loadings. The range of conditions enables quantitative assessment of the analytical approaches under carefully controlled experimental conditions. In the homogeneous system with gaseous carbon dioxide producing the carbon atomic emission signal, the standard deviation method provided a suitable metric that is directly proportional to the analyte signal and compares favorably with a traditional ensemble averaging scheme. In contrast, the applicability of the standard deviation method for analysis of non-homogenous analyte systems (e.g., aerosol systems) must be carefully considered. It was shown both experimentally and via Monte Carlo simulations that the standard deviation approach can produce an analyte response that is monotonic with analyte concentration up to a point at which the analyte signal starts to transition from a non-homogeneous system to a homogeneous systems (i.e., around a 50% sampling point for aerosol particles). In addition, the standard deviation spectrum is capable of revealing spectral locations of non-homogeneously dispersed analyte species without a priori knowledge.