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As the exploration of Mars and other solar system bodies becomes more prevalent, the importance of accurate methods in chemical analyses has increased. The use of laser‐induced breakdown spectroscopy (LIBS) in such analyses requires that well understood and accurate statistical methods exist for appropriate interpretation of resulting spectra. Many multivariate techniques have been developed for the elemental quantification of LIBS; however, each still has its limitations. In an endeavor to improve upon existing methodologies, a new algorithm is proposed using the ChemCam preflight calibration dataset and a dataset from the characterization of a LIBS/Raman sensor prototype developed at York University. The algorithm which was developed in this work is a linear mixture model within a submodel clustering framework. The cross validation and test results of the model on both datasets were reported using various metrics for each element under consideration (root mean square error, relative standard deviation, and R2 value). The algorithm was subsequently compared with other well established chemometric models on both datasets, such as principal component regression, partial least square regression, and ordinary least squares regression. Further validation of the algorithm was achieved by comparing the results presented herein to previously published results on the ChemCam data. The samples in each dataset are highly representative of Martian geology, which, given the overwhelming success of the algorithm on both datasets, suggests that subsequent implementation of the proposed algorithm on larger databases may have significant implications for Martian geochemical analyses and for planetary exploration as a whole. One of the principle requirements as exploration to space becomes more common is the biological and geochemical analysis of planets. LIBS has been used for qualitative and quantitative elemental analyses on Mars, and is likely to continue being used. In order, however, for instruments like LIBS to reach their full potential, accurate chemometric techniques are necessary. In this work, we propose a novel framework for the elemental quantification of geological samples analyzed by LIBS. [ABSTRACT FROM AUTHOR]

One of the primary objectives of planetary exploration is the search for signs of life (past, present, or future). Formulating an understanding of the geochemical processes on planetary bodies may allow us to define the precursors for biological processes, thus providing insight into the evolution of past life on Earth and other planets, and perhaps a projection into future biological processes. Several techniques have emerged for detecting biomarker signals on an atomic or molecular level, including laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy, laser-induced fluorescence (LIF) spectroscopy, and attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy, each of which addresses complementary aspects of the elemental composition, mineralogy, and organic characterization of a sample. However, given the technical challenges inherent to planetary exploration, having a sound understanding of the data provided from these technologies, and how the inferred insights may be used synergistically is critical for mission success. In this work, we present an in-depth characterization of a set of samples collected during a 28-day Mars analog mission conducted by the Austrian Space Forum in the Dhofar region of Oman. The samples were obtained under high-fidelity spaceflight conditions and by considering the geological context of the test site. The specimens were analyzed using the LIBS–Raman sensor, a prototype instrument for future exploration of Mars. We present the elemental quantification of the samples obtained from LIBS using a previously developed linear mixture model and validated using scanning electron microscopy energy dispersive spectroscopy. Moreover, we provide a full mineral characterization obtained using ultraviolet Raman spectroscopy and LIF, which was verified through ATR FT-IR. Lastly, we present possible discrimination of organics in the samples using LIF and time-resolved LIF. Each of these methods yields accurate results, with low errors in their predictive capabilities of LIBS (median relative error ranging from 4.5% to 16.2%), and degree of richness in subsequent inferences to geochemical and potential biochemical processes of the samples. The existence of such methods of inference and our ability to understand the limitations thereof is crucial for future planetary missions, not only to Mars and Moon but also for future exoplanetary exploration. Graphical Abstract [ABSTRACT FROM AUTHOR]