Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott; Morris, Richard V.; Ehlmann, Bethany; Dyar, M. Darby

doi:10.1016/j.sab.2016.12.002

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

Journal Article · Wed Dec 14 23:00:00 EST 2016 · Spectrochimica Acta. Part B, Atomic Spectroscopy

DOI:https://doi.org/10.1016/j.sab.2016.12.002· OSTI ID:1337117

^[1]; Clegg, Samuel M. ^[2]; ^[3]; Wiens, Roger C. ^[2]; McLennan, Scott ^[4]; Morris, Richard V. ^[5]; Ehlmann, Bethany ^[6]; Dyar, M. Darby ^[7]

U.S.Geological Survey Astrogeology Science Center, Flagstaff, AZ (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Copenhagen, Copenhagen (Denmark)
State Univ. of New York at Stony Brook (SUNY), Stony Brook, NY (United States)
NASA Johnson Space Center, Houston, TX (United States)
California Inst. of Technology (CalTech), Pasadena, CA (United States). Division of Geological and Planetary Sciences
Mt. Holyoke College, South Hadley, MA (United States). Dept. of Astronomy

We report that 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 “submodel” 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. Lastly, the sub-model method, using partial least squares regression (PLS), 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.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL)

Sponsoring Organization:: USDOE; NASA

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1337117

Alternate ID(s):: OSTI ID: 22755405
OSTI ID: 1693629

Report Number(s):: LA-UR-16-28669

Journal Information:: Spectrochimica Acta. Part B, Atomic Spectroscopy, Journal Name: Spectrochimica Acta. Part B, Atomic Spectroscopy; ISSN 0584-8547

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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