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Hydrogen (H) is a critical element to measure on the surface of Mars because its presence in mineral structures is indicative of past hydrous conditions. The Curiosity rover uses the laser-induced breakdown spectrometer (LIBS) on the ChemCam instrument to analyze rocks for their H emission signal at 656.6 nm, from which H can be quantified. Previous LIBS calibrations for H used small data sets measured on standards and/or manufactured mixtures of hydrous minerals and rocks and applied univariate regression to spectra normalized in a variety of ways. However, matrix effects common to LIBS make these calibrations of limited usefulness when applied to the broad range of compositions on the Martian surface. In this study, 198 naturally-occurring hydrous geological samples covering a broad range of bulk compositions with directly-measured H content are used to create more robust prediction models for measuring H in LIBS data acquired under Mars conditions. Both univariate and multivariate prediction models, including partial least square (PLS) and the least absolute shrinkage and selection operator (Lasso), are compared using several different methods for normalization of H peak intensities. Data from the ChemLIBS Mars-analog spectrometer at Mount Holyoke College are compared against spectra from the same samples acquired using a ChemCam-like instrument at Los Alamos National Laboratory and the ChemCam instrument on Mars. Results show that all current normalization and data preprocessing variations for quantifying H result in models with statistically indistinguishable prediction errors (accuracies) ca. ± 1.5 weight percent (wt%) H 2 O, limiting the applications of LIBS in these implementations for geological studies. This error is too large to allow distinctions among the most common hydrous phases (basalts, amphiboles, micas) to be made, though some clays (e.g., chlorites with ≈ 12 wt% H 2 O, smectites with 15–20 wt% H 2 O) and hydrated phases (e.g., gypsum with ≈ 20 wt% H 2 O) may be differentiated from lower-H phases within the known errors. Analyses of the H emission peak in Curiosity calibration targets and rock and soil targets on the Martian surface suggest that shot-to-shot variations of the ChemCam laser on Mars lead to variations in intensity that are comparable to those represented by the breadth of H standards tested in this study. [ABSTRACT FROM AUTHOR]

This project examines the causes, effects, and optimization of continuum removal in laser-induced breakdown spectroscopy (LIBS) to produce the best possible prediction accuracy of elemental composition in geological samples. We compare prediction accuracy resulting from several different techniques for baseline removal, including asymmetric least squares (ALS), adaptive iteratively reweighted penalized least squares (Air-PLS), fully automatic baseline correction (FABC), continuous wavelet transformation, median filtering, polynomial fitting, the iterative thresholding Dietrich method, convex hull/rubber band techniques, and a newly-developed technique for Custom baseline removal (BLR). We assess the predictive performance of these methods using partial least-squares analysis for 13 elements of geological interest, expressed as the weight percentages of SiO 2 , Al 2 O 3 , TiO 2 , FeO, MgO, CaO, Na 2 O, K 2 O, and the parts per million concentrations of Ni, Cr, Zn, Mn, and Co. We find that previously published methods for baseline subtraction generally produce equivalent prediction accuracies for major elements. When those pre-existing methods are used, automated optimization of their adjustable parameters is always necessary to wring the best predictive accuracy out of a data set; ideally, it should be done for each individual variable. The new technique of Custom BLR produces significant improvements in prediction accuracy over existing methods across varying geological data sets, instruments, and varying analytical conditions. These results also demonstrate the dual objectives of the continuum removal problem: removing a smooth underlying signal to fit individual peaks (univariate analysis) versus using feature selection to select only those channels that contribute to best prediction accuracy for multivariate analyses. Overall, the current practice of using generalized, one-method-fits-all-spectra baseline removal results in poorer predictive performance for all methods. The extra steps needed to optimize baseline removal for each predicted variable and empower multivariate techniques with the best possible input data for optimal prediction accuracy are shown to be well worth the slight increase in necessary computations and complexity. [ABSTRACT FROM AUTHOR]

This study uses 1356 spectra from 452 geologically-diverse samples, the largest suite of LIBS rock spectra ever assembled, to compare the accuracy of elemental predictions in models that use only spectral regions thought to contain peaks arising from the element of interest versus those that use information in the entire spectrum. Results show that for the elements Si, Al, Ti, Fe, Mg, Ca, Na, K, Ni, Mn, Cr, Co, and Zn, univariate predictions based on single emission lines are by far the least accurate, no matter how carefully the region of channels/wavelengths is chosen and despite the prominence of the selected emission lines. An automated iterative algorithm was developed to sweep through all 5485 channels of data and select the single region that produces the optimal prediction accuracy for each element using univariate analysis. For the eight major elements, use of this technique results in a 35% improvement in prediction accuracy; for minors, the improvement is 13%. The best wavelength region choice for any given univariate analysis is likely to be an inherent property of the specific training set that cannot be generalized. In comparison, multivariate analysis using partial least-squares (PLS) almost universally outperforms univariate analysis. PLS using all the same wavelength regions from the univariate analysis produces results that improve in accuracy by 63% for major elements and 3% for minor element. This difference is likely a reflection of signal to noise ratios, which are far better for major elements than for minor elements, and likely limit their prediction accuracy by any technique. We also compare predictions using specific wavelength ranges for each element against those employing all channels. Masking out channels to focus on emission lines from a specific element that occurs decreases prediction accuracy for major elements but is useful for minor elements with low signals and proportionally much higher noise; use of PLS rather than univariate analysis is still recommended. Finally, we tested the generalizability of our results by analyzing a second data set from a different instrument. Overall prediction accuracies for the mixed data sets are higher than for either set alone for all major and minor elements except Ni, Cr, and Co, where results are roughly comparable. [ABSTRACT FROM AUTHOR]

Quantitative analysis methods in all applications require robust measurements of quantification limits (LOQ); the threshold at which predicted values can be trusted. Although established LOQ protocols exist for univariate techniques, based on classical peak area measurements that vary directly with concentration of analytes, there is limited work on how best to quantify measurement limits when analyzed using more modern multivariate analysis (MVA) methods. This is especially the case for the technique of laser-induced breakdown spectroscopy (LIBS), which in the last decade has emerged as a promising technique for elemental analyses in many LIBS applications, wherein MVA modelling techniques outperform univariate methods. Accordingly, this study provides the final step in a protocol for generating robust quantification models for LIBS, with carefully determined accuracies for geological applications within. Its goal is to provide a template for calculating LOQ based on multivariate LIBS regression models, and to understand how this value is affected by several external factors: instrumentation (Mount Holyoke College ChemLIBS vs. Los Alamos National Laboratory ChemCam flight model), outlier removal method (highest natural concentration vs. statistically-based method), and acquisition atmosphere (Mars vs. air vs. vacuum), for two methods of calculating instrument sensitivity from experimental data (taken from metal spectra vs. noise-free regions of rock spectra). Partial-least squares regression models are made for almost every combination of the aforementioned parameters for the major oxides MnO, Na 2 O, SiO 2 and minor and trace elements Li, Ni, Pb, Rb, Sr, and Zn. ChemLIBS models use 2607 geochemical standards, while ChemCam and ChemLIBS comparison models use 205 standards common to both instrument's datasets. Because LOQs determine the minimum predicted concentration to have confidence in, these values have a direct impact on model testing errors. The effect of LOQ on model validation is also summarized by comparing test accuracies before and after LOQ application. This step is essential to fully understand appropriate quantification of rock geochemistries with LIBS. Results show that LOQ is influenced by the variety within the composition of training standards and penalizes regression vectors that over- or under-fit the calibration data. These characteristics make LOQ an essential metric for better understanding model quality 'behind the scenes', in addition to common measures like test accuracy and R2 correlation. Applying LOQ to MVA models is therefore especially important when quantifying elements within such complex standards as rocks. [Display omitted] • A reproducible methods for calculating LOQ of multivariate models is outlined. • Tested effects of instrumentation, outlier limits, and atmosphere on LOQ in rocks. • LOQ penalizes regression vectors that over- or under-fit training data. • LOQ is influenced by compositional variance within training standards. • LOQ is an essential metric to better understand multivariate model quality. [ABSTRACT FROM AUTHOR]

Abstract: The key to understanding the sulfur history on Mars is to identify and determine sulfate and sulfide compositions and then to draw from them geologic clues about their environments of formation. To lay a foundation for use of remote LIBS to sulfur analysis in planetary exploration, we have undertaken a focused study of sulfur LIBS in geological samples in a simulated Mars atmosphere, with experimental parameters replicating the ChemCam LIBS instrument. A suite of twelve samples was selected, including rocks rich in minerals representative of sulfates and sulfides that might be encountered on Mars. Univariate analysis of sulfur emission lines did not provide quantitative information. Partial least squares (PLS) analysis was successful at modeling sulfur concentrations for a subset of samples with similar matrices. Sulfide minerals were identified on the basis of other siderophile or chalcophile peaks, such as those arising from Zn and Cu. Because the S lines are very weak compared to those of other elements, optimal PLS results were obtained by restricting the wavelength range to channels close to the most intense sulfur lines ~540–570nm. Principal components analysis was attempted on the dataset, but did not differentiate the samples into meaningful groups because the sulfur lines are not strong enough. However, areas of the relatively weak S, H, and O peaks may be used to correctly classify all samples. Based on these outcomes, a flowchart that outlines a possible decision tree for identification and quantification of sulfur in remote LIBS analysis was constructed. Results suggest that LIBS data acquired under Mars conditions can meet the science requirements for the ChemCam instrument. [Copyright &y& Elsevier]

Laser-induced breakdown spectroscopy (LIBS) data acquired from 2959 geochemical standards allow the effects of training set size on LIBS accuracy in geochemical analyses to be evaluated. In addition, LIBS prediction accuracies are quantified for 65 elements based on a typical benchtop instrument. Analyses used two equivalent randomly selected subsets of the full data set to compare prediction accuracies of partial least squares models using 75, 50, 25, 10, 5, 2.5, 1, and 0.5% of the total data set for training and the remainder for testing. The number of components, a measure of complexity, in the PLS models was shown to increase with the size of the training set. Based on root mean square errors on unseen test data, our results show that the larger the training set, the better (lower) the prediction accuracy will be on unseen data. Calibration (training set) size was shown to have a first-order effect on prediction accuracy relative to spectral resolution and detector sensitivity. Different methods of assessing model accuracy using root mean square error (RMSE) are compared, including the error of the calibration (RMSE-C), the error of cross-validation (RMSE-CV), and the error of prediction (RMSE-P). Use of RMSE-C is inappropriate because the samples being predicted are those on which the model was trained. In data sets that are sufficiently large, use of test data (RMSE-P) provides the best measure of prediction accuracy, while RMSE-CV is useful only to provide an estimate of subsequent model performance. Increasing the number of cross-validation folds for our large dataset yields surprisingly comparable RMSE-CV values for models with five or more (up to 100) folds, but this result is likely not applicable to smaller data sets and needs further evaluation. Unlabelled Image • For geological samples, LIBS accuracy improves with larger training sets by ≥31% • RMSE-C, RMSE-CV, and RMSE-P accuracies are quantified for 65 elements • Use of held-out test provides the best measure of model accuracy • Leave-one-out cross-validation can estimate model performance for smaller data sets [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) is valued for its ability to remotely detect a wide range of elements, including light elements, under a variety of atmospheric conditions. This study uses LIBS spectra of 402 rock standards to quantify lithium (Li), boron (B), carbon/carbon dioxide (C/CO 2), and sulfur (S) in Mars and Earth atmospheres and under vacuum. Two regression methods were tested: univariate analysis (UVA), here using peak areas to predict concentrations, and multivariate analysis (MVA). Partial least squares (PLS) and the least absolute shrinkage and selection operator (lasso) use information from larger regions of LIBS spectra. Rock powders were doped with up to 10 wt% of each light element to help identify strongly correlated peaks for UVA. UVA and MVA models were assessed using root mean square errors (RMSEs) of cross-validation (CV), calibration RMSEs, and R2 correlation between predicted and true concentrations. Li had the most strongly correlated peaks, similar UVA and MVA model performance, and the lowest relative prediction errors. B and C had few weakly-correlated peaks, leading to extremely poor UVA R2 correlations despite having similar RMSEs to MVA models with mediocre performance. S had no visible peaks in our LIBS setup and as a result, MVA models had extremely high prediction errors. Model performance was not significantly affected by atmospheric differences despite visible changes in peak appearance, as long as model and test data were acquired under identical conditions. PLS regression on the entire LIBS spectrum consistently created models with the lowest quantification errors and highest R2 correlations. Light element predictions may be improved using higher resolution, gated spectrometers that cover a wider wavelength range than those used in our setup, which matches ChemCam. Unlabelled Image • Average best RMSE-CVs of ±27 ppm Li, ±40 ppm B, ±1.46 wt% CO 2 , and ± 513 ppm S • Average best RMSE-Cs of ±11 ppm Li, ±28 ppm B, ±1.04 wt% CO 2 , and ± 384 ppm S. • Light elements were best quantified using multivariate analysis on entire spectrum. • No significant accuracy differences among LIBS models in Mars, Earth, or vacuum • R2 correlations show multivariate models are more accurate than univariate models. [ABSTRACT FROM AUTHOR]

The accuracy of laser-induced breakdown spectroscopy (LIBS) methods for analyzing geological samples is improved when calibration standards and unknown targets are compositionally similar. A recent study suggests that customized submodels can be used to optimize calibration datasets to achieve more accurate predictions [1]. In practice, this is difficult to implement because the errors inherent in the methods used for sorting unknown targets by composition may affect how successfully this matching can occur. Moreover, creation of submodels intrinsically reduces the size of the dataset on which the model is trained, which has been shown to reduce prediction accuracy. This paper uses LIBS spectra of 2990 unique rock powder standards to compare the accuracy of 1) submodels generated for each element over its geochemical range, 2) submodels created using SiO 2 content only, 3) submodels created using the ratio of Si(II)/Si(I) emission lines to group spectra by a proxy for approximate plasma temperature, and 4) models created using all data. Results indicate that prediction accuracies are not always improved by creating submodels because subdividing a dataset to optimize calibrations will always result in a smaller database available for each submodel, and the reduced training set size negatively affects accuracy. Customized LIBS standards for specific applications might overcome this problem in cases where the matrix is similar and the expected concentration range is known. But in a majority of geochemical applications, submodel approaches are only useful in improving prediction accuracies when the initial database is itself extensive enough to support large, robust submodel calibration suites. [Display omitted] • LIBS spectra are divided into submodels to predict major element compositions. • Submodels are compared to calibrations generated with all calibration spectra. • More accurate RMSEPs are often found using the full range of calibration spectra. [ABSTRACT FROM AUTHOR]

Abstract: Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples. [Copyright &y& Elsevier]

The recent manufacture of portable laser-induced breakdown spectroscopy (pLIBS) instruments has permitted widespread applications of an established analytical technique for in situ geochemical quantifications. This novelty has created a need for investigations comparing the default univariate pLIBS calibration accuracies to custom instrument calibrations based on geological samples and to other modeling methods. Previous research has shown that multivariate models can mitigate some of the matrix effects that cause intensity and concentration non-linearity within LIBS plasmas of rocks. This study thus makes two pLIBS calibration comparisons: one compares partial least squares (PLS) multivariate regression to univariate instrument models, while the other compares the default instrument calibration, intended for general industrial use, to a custom instrument calibration based on rock standards. Both comparisons use root mean squared errors (RMSE) between model-predicted and true standard values of the major oxides Al 2 O 3 , CaO, Fe 2 O 3 , K 2 O, MgO, MnO, Na 2 O, P 2 O 5 , SiO 2 , and TiO 2 , and minor and trace elements Ba, Cr, Cu, H 2 O, Li, Ni, Pb, S, and Zn to test calibration accuracies. pLIBS spectra from 2007 diverse rock standards were used for the PLS versus univariate model comparison, while 89 publicly-available geochemical standards were used to compare the default and custom instrument calibrations. PLS models give test accuracies for major elements of ±8.61 wt% for SiO 2 , ±3.38 for Al 2 O 3 , ±0.77 for TiO 2 , ±3.84 for Fe 2 O 3 , ±4.41 wt% for MgO, ±3.08 wt% for CaO, ±1.09 wt% for Na 2 O, and ±1.52 wt% for K 2 O. Quantification of trace and minor elements was more accurate with multivariate regression than with univariate instrument models, and the new custom instrument calibration had lower prediction errors for these elements than those of the default models. However, minor and trace element errors from all three calibrations were often greater than the average concentration of the test standards, which suggests that these substances are not well quantified in rocks by pLIBS instruments. Unlabelled Image • Major oxides were predicted equally well among instrument and multivariate models. • PLS models gave much more accurate test predictions for trace and minor elements. • Large & diverse test standard suites give more robust instrument model accuracies. • Accuracy results appear consistent with other benchtop LIBS test errors. • Custom instrument models were generally more accurate than default calibrations. [ABSTRACT FROM AUTHOR]

The task of proper baseline or continuum removal is common to nearly all types of spectroscopy. Its goal is to remove any portion of a signal that is irrelevant to features of interest while preserving any predictive information. Despite the importance of baseline removal, median or guessed default parameters are commonly employed, often using commercially available software supplied with instruments. Several published baseline removal algorithms have been shown to be useful for particular spectroscopic applications but their generalizability is ambiguous. The new Custom Baseline Removal (Custom BLR) method presented here generalizes the problem of baseline removal by combining operations from previously proposed methods to synthesize new correction algorithms. It creates novel methods for each technique, application, and training set, discovering new algorithms that maximize the predictive accuracy of the resulting spectroscopic models. In most cases, these learned methods either match or improve on the performance of the best alternative. Examples of these advantages are shown for three different scenarios: quantification of components in near-infrared spectra of corn and laser-induced breakdown spectroscopy data of rocks, and classification/matching of minerals using Raman spectroscopy. Software to implement this optimization is available from the authors. By removing subjectivity from this commonly encountered task, Custom BLR is a significant step toward completely automatic and general baseline removal in spectroscopic and other applications. [ABSTRACT FROM AUTHOR]

Obtaining quantitative chemical information using laser-induced breakdown spectroscopy is challenging due to the variability in the bulk composition of geological materials. Chemical matrix effects caused by this variability produce changes in the peak area that are not proportional to the changes in minor element concentration. Therefore the use of univariate calibrations to predict trace element concentrations in geological samples is plagued by a high degree of uncertainty. This work evaluated the accuracy of univariate minor element predictions as a function of the composition of the major element matrices of the samples and examined the factors that limit the prediction accuracy of univariate calibrations. Five different sample matrices were doped with 10-85 000 ppm Cr, Mn, Ni, Zn, and Co and then independently measured in 175 mixtures by X-ray fluorescence, inductively coupled plasma atomic emission spectrometry, and laser-induced breakdown spectroscopy, the latter at three different laser energies (1.9, 2.8, and 3.7 mJ). Univariate prediction models for minor element concentrations were created using varying combinations of dopants, matrices, normalization/no normalization, and energy density; the model accuracies were evaluated using root mean square prediction errors and leave-one-out cross-validation. The results showed the superiority of using normalization for predictions of minor elements when the predicted sample and those in the training set had matrices with similar SiO2 contents. Normalization also mitigates differences in spectra arising from laser/sample coupling effects and the use of different energy densities. Prediction of minor elements in matrices that are dissimilar to those in the training set can increase the uncertainty of prediction by an order of magnitude. Overall, the quality of a univariate calibration is primarily determined by the availability of a persistent, measurable peak with a favorable transition probability that has little to no interference from neighboring peaks in the spectra of both the unknown and those used to train it. [ABSTRACT FROM AUTHOR]

The ChemCam Laser-Induced Breakdown Spectroscopy (LIBS) instrument onboard the Mars Science Laboratory (MSL) rover Curiosity has obtained > 300,000 spectra of rock and soil analysis targets since landing at Gale Crater in 2012, and the spectra represent perhaps the largest publicly-available LIBS datasets. The compositions of the major elements, reported as oxides (SiO 2 , TiO 2 , Al 2 O 3 , FeO T , MgO, CaO, Na 2 O, K 2 O), have been re-calibrated using a laboratory LIBS instrument, Mars-like atmospheric conditions, and a much larger set of standards (408) that span a wider compositional range than previously employed. The new calibration uses a combination of partial least squares (PLS1) and Independent Component Analysis (ICA) algorithms, together with a calibration transfer matrix to minimize differences between the conditions under which the standards were analyzed in the laboratory and the conditions on Mars. While the previous model provided good results in the compositional range near the average Mars surface composition, the new model fits the extreme compositions far better. Examples are given for plagioclase feldspars, where silicon was significantly over-estimated by the previous model, and for calcium-sulfate veins, where silicon compositions near zero were inaccurate. The uncertainties of major element abundances are described as a function of the abundances, and are overall significantly lower than the previous model, enabling important new geochemical interpretations of the data. [ABSTRACT FROM AUTHOR]

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the laser-induced breakdown spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element's emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares (PLS) regression, is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements. [ABSTRACT FROM AUTHOR]

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k -nearest neighbor ( k NN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO 2 , Fe 2 O 3 , CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na 2 O, K 2 O, TiO 2 , and P 2 O 5 , the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels) relative to the small number of samples studied. The best-performing models were SVR-Lin for SiO 2 , MgO, Fe 2 O 3 , and Na 2 O, lasso for Al 2 O 3 , elastic net for MnO, and PLS-1 for CaO, TiO 2 , and K 2 O. Although these differences in model performance between methods were identified, most of the models produce comparable results when p ≤ 0.05 and all techniques except kNN produced statistically-indistinguishable results. It is likely that a combination of models could be used together to yield a lower total error of prediction, depending on the requirements of the user. [ABSTRACT FROM AUTHOR]

An integrated Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) instrument is a valuable geoanalytical tool for future planetary missions to Mars, Venus, and elsewhere. The ChemCam instrument operating on the Mars Curiosityrover includes a remote LIBS instrument. An integrated Raman-LIBS spectrometer (RLS) based on the ChemCam architecture could be used as a reconnaissance tool for other contact instruments as well as a primary science instrument capable of quantitative mineralogical and geochemical analyses. Replacing one of the ChemCam spectrometers with a miniature transmission spectrometer enables a Raman spectroscopy mineralogical analysis to be performed, complementing the LIBS chemical analysis while retaining an overall architecture resembling ChemCam. A prototype transmission spectrometer was used to record Raman spectra under both Martian and Venus conditions. Two different high-pressure and high-temperature cells were used to collect the Raman and LIBS spectra to simulate surface conditions on Venus. The resulting LIBS spectra were used to generate a limited partial least squares Venus calibration model for the major elements. These experiments demonstrate the utility and feasibility of a combined RLS instrument. [ABSTRACT FROM AUTHOR]