Low‐level LIBS and Raman data fusion in the context of in situ Mars exploration

Abstract

Laser‐induced breakdown spectroscopy (LIBS) and Raman spectroscopy are powerful key techniques for the geoanalytical exploration of extraterrestrial bodies, especially when combined. Their data are complementary, which motivates the question of how it can be best combined to maximize the scientific output. For this study, LIBS and Raman data from pure sulfates and their mixtures as well as from other Mars‐relevant salts such as carbonates, chlorides, perchlorates, and sulfates in a basaltic matrix were measured and investigated. All measurements were performed with miniaturized setups, and LIBS experiments were done in simulated Martian atmospheric conditions. Multivariate data analysis (MVA) techniques such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA) were employed to evaluate the potential for identifying the sulfates or the salts in the basalt with LIBS and Raman data alone and with their low‐level fused data. We found that low‐level data fusion, that is, combination of LIBS and Raman spectra at the data level, can improve the identification of sulfates and salts. Although the approach of low‐level data fusion aims to use all relevant information from both techniques, we observed that not all benefits from the single models are completely represented by the fused model. The computation and performance of appropriate MVA models are affected by the weighting of the single spectra in the combined one, by the dimensionality of the MVA models, and in case of PLS‐DA, by the given input data. From this study, we conclude that generally, data fusion of LIBS and Raman is an advantage for the identification of unknown samples but that more levels, especially, high‐level data fusion (decision level), should be further investigated.

Detecting sulfur on the Moon: The potential of vacuum ultraviolet laser-induced breakdown spectroscopy

“…In vacuum, on the other hand, the high degree of ionization reached during plasma initiation is conserved because the lack of confinement and the inherent long mean free path length hamper efficient electron-ion recombination. This suggests an already higher degree of ionization for a laser-induced plasma in vacuum, as reported in other publications [5,21,117]. Higher laser powers cause an even higher degree of ionization during the plasma formation, which is then maintained and results in stronger emission lines from high ionization states.…”

“…Although the sample consists of 87 wt% lunar simulant even for the highest investigated S concentration and the matrix should therefore not be significantly altered, the reproducibility of this non-linear behaviour suggests that it is inherent in samples of S mixed in LHS. The saturation at higher concentrations could be due to self-absorption [66], which is, in general, expected to be low in vacuum owing to the low density of the freely expanding plasma but has been reported in different publications [117,131].…”

Machine learning for recognizing minerals from multispectral data

“…In spectroscopy, it has been shown that different analytical techniques, such as Raman, VNIR, and LIBS we are evaluating, provide complementary information to each other. Specifically, the combination of LIBS together with hyperspectral images in VNIR/SWIR range (Haavisto et al, 2013) as well as combining Raman spectroscopy with Laser-induced fluorescence (Kauppinen et al, 2014) or LIBS (Rammelkamp et al, 2019) spectra have been favorably evaluated.…”

GeoRaman

“…Schröder et al [22] investigate the effects of different conditions, simulating Mars environment, on LIBS and Raman measurements of Fe, Ni, Ti and their oxides. Rammelkamp et al [23] shown that the low-level fusion of Raman and LIBS data is helpful in the multivariate analysis of Mars-relevant salts such as sulfates and their mixtures as well as carbonates, chlorides and perchlorates. Manrique-Martinez et al [24] compare data obtained by LIBS and Raman spectroscopy by evaluating the contribution of both analytical techniques with subsequent univariate and multivariate statistical techniques.…”