Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions. (15th July 2015)

• Record Type: Journal Article
• Authors: Boucher, Thomas; Carey, CJ; Dyar, Melinda Darby; Mahadevan, Sridhar; Clegg, Samuel; Wiens, Roger
• Is Part Of: Journal of chemometrics. Volume 29:Number 9(2015:Sep.)
• Page Start: 484
• Page End: 491
• Publication Date: 2015-07-15
• Subjects: laser‐induced breakdown spectroscopy -- locally linear embedding -- manifold learning -- space instrumentation; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2727
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract : 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 © 2015 John Wiley & Sons, Ltd. Abstract : The machine learning algorithm locally linear embedding (LLE) is used as a preprocessing step for laser‐induced breakdown spectroscopy (LIBS) data to improve the predictive performance of partial least squares calibration models. LLE is a manifold learning method that provides a low‐dimensional representation of high‐dimensional data, which attempts to preserve the geometric structure underlying the data. A novel variant, LLE for regression, is also introduced and shown to outperform traditional LLE for predicting most major elements in the mineral samples. The effectiveness of the algorithms is shown using three different LIBS datasets recorded under Mars‐like conditions for the Mars Science Laboratory team.