Laser-induced breakdown spectroscopy; LIBS; Variable angle; Incidence angle; Collection angle; Partial least-squares analysis; PLS;

Laser-induced breakdown spectroscopy has become a popular tool for rapid elemental analysis of geological materials. However, quantitative applications of LIBS are plagued by variability in collected spectra that cannot be attributed to differences in geochemical composition. Even under ideal laboratory conditions, variability in LIBS spectra creates a host of difficulties for quantitative analysis. This is only exacerbated during field work, when both the laser-sample distance and the angle of ablation/collection are constantly changing. A primary goal of this study is to use empirical evidence to provide a more accurate assessment of uncertainty in LIBS-derived element predictions. We hope to provide practical guidance regarding the angles of ablation and collection that can be tolerated without substantially increasing prediction uncertainty beyond that which already exists under ideal laboratory conditions. Spectra were collected from ten geochemically diverse samples at angles of ablation and collection ranging from 0 degrees to +/- 60 degrees. Ablation and collection angles were changed independently and simultaneously in order to isolate spectral changes caused by differences in ablation angle from those due to differences in collection angle. Most of the variability in atomic and continuum spectra is attributed to changes in the ablation angle, rather than the collection angle. At higher angles, the irradiance of the laser beam is lower and produces smaller, possibly less dense plasmas. Simultaneous changes in the collection angle do not appear to affect the collected spectra, possibly because smaller plasmas are still within the viewing area of the collection optics, even though this area is reduced at higher collection angles. A key observation is that changes in the magnitude of atomic and total emission are <5% and 10%, respectively, in spectra collected with the configuration that most closely resembles field measurements (W) at angles <20 degrees. In addition, variability in atomic and continuum emission is strongly dependent upon sample composition. Denser, more Fe/Mg-rich rocks exhibited much less variability with changes in ablation and collection angles than Si-rich felsic rocks. Elemental compositions of our variable angle data that were predicted using a much larger but conventionally-collected calibration suite show that accuracy generally suffers when the incidence and collection angles are high. Prediction accuracy (for measurements acquired with varying collection and ablation angles) varies from +/- 1.28-1.86 wt% for Al2O3, +/- 1.25-1.66% wt for CaO, +/- 1.90-2.21 wt% for Fe2O3T, +/- 0.76-0.94 wt% for K2O, +/- 2.85-3.61 wt% MgO, +/- 0.15-0.17 wt% for MnO, +/- 0.68-0.78 wt% for Na2O, +/- 0.33-0.42 wt% for TiO2, and +/- 2.94-4.34 wt% SiO2. The ChemCam team is using lab data acquired under normal incidence and collection angles to predict the compositions of Mars targets at varying angles. Thus, the increased errors noted in this study for high incidence angle measurements are likely similar to additional, unacknowledged errors on ChemCam results for non-normal targets analyzed on Mars. Optimal quantitative analysis of LIBS spectra must include some knowledge of the angle of ablation and collection so the approximate increase in uncertainty introduced by a departure from normal angles can be accurately reported. (C) 2017 Elsevier B.V. All rights reserved.

Baseline removal; Raman spectroscopy; laser-induced breakdown spectroscopy; LIBS; very near; infrared spectroscopy; VNIR;X-RAY-SPECTRA; LEAST-SQUARES; RAMAN-SPECTRA; NMR-SPECTRA; RECOGNITION; SUBTRACTION; REGRESSION; ALGORITHM

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 spectroscopydata 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.

Laser-induced breakdown spectroscopy; LIBS; matrix effects; univariate calibration; minor elements; Cr; Ni; Co; Zn; Mn;GALE CRATER; MARS; SAMPLES

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.