Automated detection of element-specific features in LIBS spectra

Abstract

This work introduces a novel semi-automatic approach to identify elemental lines in spectra obtained via laser-induced breakdown spectroscopy (LIBS). The algorithm is based on unique spectral fingerprints of individual elements that are configured into comb-like filters. The element-specific filters are then correlated with measured spectra for semi-supervised qualitative analysis of samples. Spectral variations are accommodated by adjusting the micro-parameters of the comb filter. This step ensures accurate results despite minor deviations from the instrument's ideal calibration due to instrumental fluctuations, e.g., drift in spectral calibration or line broadening. Additionally, the algorithm can autonomously detect spectral interference regions, aiding the analyst in verifying spectral lines where such interference may occur. The paper presents a comprehensive overview of the algorithm and discusses the main concepts, parameters, optimization steps, and limitations using Echelle spectra of two standard reference materials with different complexity: borosilicate glass (NIST 1411) and low-alloyed steel (SUS1R). Furthermore, the transferability of the approach to different scenarios and real-life applications is demonstrated using a single-channel Czerny-Turner spectrum of an amalgam filling extracted from a hyperspectral image of a human tooth. A demo of the algorithm is publicly available for non-commercial purposes.This work introduces a novel semi-automatic approach to identify elemental lines in spectra obtained via laser-induced breakdown spectroscopy (LIBS). The algorithm is based on unique spectral fingerprints of individual elements that are configured into comb-like filters. The element-specific filters are then correlated with measured spectra for semi-supervised qualitative analysis of samples. Spectral variations are accommodated by adjusting the micro-parameters of the comb filter. This step ensures accurate results despite minor deviations from the instrument's ideal calibration due to instrumental fluctuations, e.g., drift in spectral calibration or line broadening. Additionally, the algorithm can autonomously detect spectral interference regions, aiding the analyst in verifying spectral lines where such interference may occur. The paper presents a comprehensive overview of the algorithm and discusses the main concepts, parameters, optimization steps, and limitations using Echelle spectra of two standard reference materials with different complexity: borosilicate glass (NIST 1411) and low-alloyed steel (SUS1R). Furthermore, the transferability of the approach to different scenarios and real-life applications is demonstrated using a single-channel Czerny-Turner spectrum of an amalgam filling extracted from a hyperspectral image of a human tooth. A demo of the algorithm is publicly available for non-commercial purposes.