Thomas Boucher

Proximal methods for calibration transfer

Calibration transfer (CT) is the process of transferring a calibration curve from one instrument to another or from one set of conditions to another. Direct standardization (DS) of the spectra from a source to a target representation is a popular method of CT, but the multivariate objective function is often significantly underdetermined. Piecewise DS regularizes DS by assuming only local differences between source and target spectra but requires the same wavelength sampling between instruments. In this work, a regularization framework from the field of convex optimization, proximal regularizers, is introduced to standardize instruments that sample at different wavelength ranges and where the differences may have global effects on the spectra. In this framework, penalty terms are appended to the DS objective function to enforce certain behaviors in the transfer matrix and the resulting transferred spectra, including sparsity and smoothness. This framework is shown to be effective at transferring spectra from a source near‐infrared instrument with a narrow wavelength range to a target instrument with a much wider wavelength range. This is demonstrated using two publicly available near‐infrared datasets.

A Fully Customized Baseline Removal Framework for Spectroscopic Applications

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.

Matrix Effects in Quantitative Analysis of Laser-Induced Breakdown Spectroscopy (LIBS) of Rock Powders Doped with Cr, Mn, Ni, Zn, and Co:

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.

Manifold preprocessing for laser-induced breakdown spectroscopy under Mars conditions

Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright

Successes and Challenges of Laser-Induced Breakdown Spectroscopy (LIBS) Applied to Chemical Analyses of Geological Samples.

The laser-induced breakdown spectrometer (LIBS) on the ChemCam instrument [1] on the Curiosity rover on Mars has played a vital role in bringing this new technology to the attention of the scientific community. With over 100,000 spectra from individual laser shots collected to date and many more in progress, ChemCam has amply demonstrated the robustness of this technique for remote applications in difficult environments. However, the ±10-20% accuracy for major and minor elements analyzed by the ChemCam LIBS [2] currently limits its usefulness to semi-quantitative analyses; comparable issues also limit other geological applications of LIBS in scenarios with geologically diverse samples. This paper discusses successes and challenges being addressed in improving LIBS accuracies and bringing this technique to its full potential in applications across the geological community.