Optimization of laser-induced breakdown spectroscopy for rapid geochemical analysis

Laser-induced breakdown spectroscopy (LIBS) is demonstrated as a quantitative technique for geochemical analysis. This study demonstrates the applicability of LIBS to bulk elemental analysis of igneous rock powders. LIBS spectra of 100 igneous rocks with highly varying compositions were acquired at 9 m standoff distance under Mars atmospheric conditions. LIBS spectra were modeled using partial least squares regressions to predict major element compositions. A series of comparative tests determined the most effective methodologies for pre-processing of spectral and compositional data, and choice of calibration set. In the best cases, calculated 1−σ errors are 1.6 wt.% SiO2, 1.5 wt.% Al2O3, 0.4 wt.% TiO2, 1.2 wt.% Fe2O3T, 1.6 wt.% MgO, 0.02 wt.% MnO, 1.1 wt.% CaO, 0.5 wt.% Na2O, 0.2 wt.% P2O5, and 0.4 wt.% K2O, with totals near 100%. The largest improvement came as a result of scaling the elemental distributions to equalize the ranges of variability. Optimal predictions for this data set were produced with calibration set compositions input as weight % oxides and not atomic fractions. Predictions were also improved when calibration sets represented the smallest range of compositional variability possible, and completely encompassed the compositional range encountered. Multiple calibration sets relevant to different rock types are preferred over a single all-encompassing calibration set. Baseline removal and transforming spectral data by their first derivative do not improve predictions and can even have negative effects. These results are directly applicable to spectra that will be acquired by the ChemCam experiment on Mars Science Laboratory, but also apply more broadly to terrestrial LIBS applications.

Research Highlights► LIBS analysis of geologic materials can provide quantitative geochemical information. ► PLS regressions yield predictions better than 1.5 wt.% oxide for major elements. ► Use of wt.% oxide is favored of atomic fractions. ► Weighting input compositions equally improves predictions. ► Carefully selected calibration sets improve predictions over random sets.