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8 - Laser-Induced Breakdown Spectroscopy

Theory and Laboratory Spectra of Geologic Materials

from Part I - Theory of Remote Compositional Analysis Techniques and Laboratory Measurements

Published online by Cambridge University Press:  15 November 2019

By
Samuel M. Clegg ,
Ryan B. Anderson  and
Noureddine Melikechi
Edited by
Janice L. Bishop ,
James F. Bell III  and
Jeffrey E. Moersch
Janice L. Bishop
Affiliation:
SETI Institute, California
James F. Bell III
Affiliation:
Arizona State University
Jeffrey E. Moersch
Affiliation:
University of Tennessee, Knoxville
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Summary

Laser-Induced Breakdown Spectroscopy (LIBS) is the remote elemental analysis technique used by the ChemCam instrument on the Curiosity rover. LIBS involves remotely ablating material from rocks and soils with a focused high-energy laser, which generates an optically excited plasma from which the elements in the rock or soil sample are quantitatively determined. The LIBS technique offers many advantages for remote chemical analysis. LIBS provides very rapid analyses without the need for any sample preparation. LIBS is capable of detecting all elements present above the detection limits independent of the atomic mass. LIBS quantitative analysis continues to evolve and produce accurate compositions with decreasing uncertainties. Furthermore, the matrix effects that tend to complicate most elemental analysis techniques like LIBS are increasingly exploited to extract more sample details. The focus of this chapter is to describe the current state of LIBS chemical analysis for remote planetary science.

Keywords

Laser-Induced Breakdown Spectroscopy (LIBS)plasma spectroscopyatomic emission spectroscopyelemental analysismultivariate analysis
Type
Chapter
Information
Remote Compositional Analysis
Techniques for Understanding Spectroscopy, Mineralogy, and Geochemistry of Planetary Surfaces
 , pp. 168 - 190
Publisher: Cambridge University Press
Print publication year: 2019

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References

Abdi, H. (2003) Partial least square regression (PLS regression). Encyclopedia for Research Methods for the Social Sciences, 6, 792–795.Google Scholar
Abrahamsson, C., Johansson, J., Sparén, A., & Lindgren, F. (2003) Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets. Chemometrics and Intelligent Laboratory Systems, 69, 3–12.Google Scholar
Anderson, R.B., Morris, R., Clegg, S., Bell, J. III, Humphries, S., & Wiens, R. (2011a) A comparison of multivariate and pre-processing methods for quantitative Laser-Induced Breakdown Spectroscopy of geologic samples. 42nd Lunar Planet. Sci. Conf., Abstract #1308.Google Scholar
Anderson, R.B., Morris, R.V., Clegg, S.M., et al. (2011b) The influence of multivariate analysis methods and target grain size on the accuracy of remote quantitative chemical analysis of rocks using laser induced breakdown spectroscopy. Icarus, 215, 608–627.CrossRefGoogle Scholar
Anderson, R.B., Bell, J.F. III, Wiens, R.C., Morris, R.V., & Clegg, S.M. (2012) Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy. Spectrochimica Acta B: Atomic Spectroscopy, 70, 24–32.Google Scholar
Anderson, R.B., Clegg, S.M., Frydenvang, J., et al. (2016) Improved accuracy in quantitative Laser-Induced Breakdown Spectroscopy using sub-model partial least squares. Spectrochimica Acta B: Atomic Spectroscopy, 129, 49–57.Google Scholar
Balabin, R.M. & Smirnov, S.V. (2011) Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data. Analytica Chimica Acta, 692, 63–72.Google Scholar
Blaney, D.L., Wiens, R.C., Maurice, S., et al. (2014) Chemistry and texture of the rocks at Rocknest, Gale crater: Evidence for sedimentary origin and diagenetic alteration. Journal of Geophysical Research, 119, 2109–2131.Google Scholar
Boucher, T., Carey, C.J., Dyar, M.D., Mahadevan, S., Clegg, S., & Wiens, R. (2015a) Manifold preprocessing for Laser-Induced Breakdown Spectroscopy under Mars conditions. Journal of Chemometrics, 29, 484–491.CrossRefGoogle Scholar
Boucher, T., Dyar, M.D., Carey, C.J., et al. (2015b) Calibration transfer of LIBS spectra to correct for Mars-Earth lab differences. 46th Lunar Planet. Sci. Conf., Abstract #2773.Google Scholar
Carroll, P. & Kennedy, E. (1981) Laser-produced plasmas. Contemporary Physics, 22, 61–96.CrossRefGoogle Scholar
Chaleard, C., Mauchien, P., Andre, N., Uebbing, J., Lacour, J., & Geertsen, C. (1997) Correction of matrix effects in quantitative elemental analysis with laser ablation optical emission spectrometry. Journal of Analytical Atomic Spectrometry, 12, 183–188.CrossRefGoogle Scholar
Ciucci, A., Corsi, M., Palleschi, V., Rastelli, S., Salvetti, A., & Tognoni, E. (1999) New procedure for quantitative elemental analysis by laser-induced plasma spectroscopy. Applied Spectroscopy, 53, 960–964.Google Scholar
Clegg, S.M., Sklute, E., Dyar, M.D., Barefield, J.E., & Wiens, R.C. (2009) Multivariate analysis of remote Laser-Induced Breakdown Spectroscopy spectra using partial least squares, principal component analysis, and related techniques. Spectrochimica Acta B: Atomic Spectroscopy, 64, 79–88.Google Scholar
Clegg, S.M., Wiens, R., Misra, A.K., et al. (2014) Planetary geochemical investigations using Raman and Laser-Induced Breakdown Spectroscopy. Applied Spectroscopy, 68, 925–936.Google Scholar
Clegg, S.M., Wiens, R.C., Anderson, R., et al. (2017) Recalibration of the Mars Science Laboratory ChemCam instrument with an expanded geochemical database. Spectrochimica Acta B: Atomic Spectroscopy, 129, 64–85.Google Scholar
Cleveland, W.S. & Devlin, S.J. (1988) Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83, 596–610.Google Scholar
Cohen, B.A., Miller, J.S., Li, Z.-H., Swindle, T.D., & French, R.A. (2014) The potassium-argon laser experiment (KArLE): In situ geochronology for planetary robotic missions. Geostandards and Geoanalytical Research, 38, 421–439.Google Scholar
Colgan, J., Judge, E.J., Johns, H.M., et al. (2015) Theoretical modeling and analysis of the emission spectra of a ChemCam standard: Basalt BIR-1A. Spectrochimica Acta B: Atomic Spectroscopy, 110, 20–30.CrossRefGoogle Scholar
Colgan, J., Barefield, J., Judge, E.J., et al. (2016) Experimental and theoretical studies of Laser-Induced Breakdown Spectroscopy emission from iron oxide: Studies of atmospheric effects. Spectrochimica Acta B: Atomic Spectroscopy, 122, 85–92.CrossRefGoogle Scholar
Cremers, D. & Radziemski, L.J. (2013) Handbook of Laser-Induced Breakdown Spectroscopy. John Wiley & Sons, Oxford.Google Scholar
Dyar, M.D., Tucker, J., Humphries, S., Clegg, S.M., Wiens, R.C., & Lane, M.D. (2011) Strategies for Mars remote Laser-Induced Breakdown Spectroscopy analysis of sulfur in geological samples. Spectrochimica Acta B: Atomic Spectroscopy, 66, 39–56.CrossRefGoogle Scholar
Dyar, M.D., Carmosino, M.L., Breves, E.A., Ozanne, M.V., Clegg, S.M., & Wiens, R.C. (2012a) Comparison of partial least squares and lasso regression techniques as applied to Laser-Induced Breakdown Spectroscopy of geological samples. Spectrochimica Acta B: Atomic Spectroscopy, 70, 51–67.CrossRefGoogle Scholar
Dyar, M.D., Carmosino, M.L., Tucker, J.M., et al. (2012b) Remote Laser-Induced Breakdown Spectroscopy analysis of East African Rift sedimentary samples under Mars conditions. Chemical Geology, 294–295, 135–151.CrossRefGoogle Scholar
Dyar, M.D., Fassett, C.I., Giguere, S., et al. (2016) Comparison of univariate and multivariate models for prediction of major and minor elements from laser-induced breakdown spectra with and without masking. Spectrochimica Acta B: Atomic Spectroscopy, 123, 93–104.Google Scholar
Eppler, A.S., Cremers, D.A., Hickmott, D.D., Ferris, M.J., & Koskelo, A.C. (1996) Matrix effects in the detection of Pb and Ba in soils using Laser-Induced Breakdown Spectroscopy. Applied Spectroscopy, 50, 1175–1181.CrossRefGoogle Scholar
Fabre, C., Cousin, A., Wiens, R., et al. (2014) In situ calibration using univariate analyses based on the onboard ChemCam targets: First prediction of martian rock and soil compositions. Spectrochimica Acta B: Atomic Spectroscopy, 99, 34–51.Google Scholar
Ferreira, E.C., Milori, D.M., Ferreira, E.J., Da Silva, R.M., & Martin-Neto, L. (2008) Artificial neural network for Cu quantitative determination in soil using a portable laser induced breakdown spectroscopy system. Spectrochimica Acta B: Atomic Spectroscopy, 63, 1216–1220.CrossRefGoogle Scholar
Feudale, R.N., Woody, N.A., Tan, H., Myles, A.J., Brown, S.D., & Ferré, J. (2002) Transfer of multivariate calibration models: A review. Chemometrics and Intelligent Laboratory Systems, 64, 181–192.CrossRefGoogle Scholar
Forni, O., Maurice, S., Gasnault, O., et al. (2013) Independent component analysis classification of Laser-Induced Breakdown Spectroscopy spectra. Spectrochimica Acta B: Atomic Spectroscopy, 86, 31–41.CrossRefGoogle Scholar
Forni, O., Gaft, M., Toplis, M.J., et al. (2015) First detection of fluorine on Mars: Implications for Gale crater’s geochemistry. Geophysical Research Letters, 42, 1020–1028.CrossRefGoogle Scholar
Giguere, S., Carey, C.J., Boucher, T., Mahadevan, S., & Dyar, M.D. (2015) An optimization perspective on baseline removal for spectroscopy. Proceedings of the 5th IJCAI Workshop on Artificial Intelligence in Space.Google Scholar
Giguere, S., Boucher, T., Carey, C.J., Mahadevan, S., & Dyar, M.D. (2017) A fully customized baseline removal framework for spectroscopic applications. Applied Spectroscopy, 71, 1457–1470.CrossRefGoogle ScholarPubMed
Gonzalez, J.J., Chirinos, J.R., Dong, M., et al. (2013) Simultaneous Laser Ablation Molecular Isotopic Spectrometry (LAMIS), Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Spectrometry (LA-ICP-MS) for elemental analysis of geological samples. Mineralogical Magazine, 77(5), Abstract #1193.Google Scholar
Hahn, D.W. & Omenetto, N. (2010) Laser-Induced Breakdown Spectroscopy (LIBS), Part I: Review of basic diagnostics and plasma–particle interactions: still-challenging issues within the analytical plasma community. Applied Spectroscopy, 64, 335A–366A.CrossRefGoogle ScholarPubMed
Jain, A.K., Murty, M.N., & Flynn, P.J. (1999) Data clustering: A review. ACM Computing Surveys (CSUR), 31, 264–323.Google Scholar
Johns, H., Kilcrease, D., Colgan, J., et al. (2015) Improved electron collisional line broadening for low-temperature ions and neutrals in plasma modeling. Journal of Physics B: Atomic, Molecular and Optical Physics, 48, 224009.CrossRefGoogle Scholar
Jolliffe, I.T. (1982) A note on the use of principal components in regression. Applied Statistics, 31, 300–303.CrossRefGoogle Scholar
King, B. (1967) Step-wise clustering procedures. Journal of the American Statistical Association, 62, 86–101.CrossRefGoogle Scholar
Knight, A.K., Scherbarth, N.L., Cremers, D.A., & Ferris, M.J. (2000) Characterization of Laser-Induced Breakdown Spectroscopy (LIBS) for application to space exploration. Applied Spectroscopy, 54, 331–340.CrossRefGoogle Scholar
Kochelek, K.A., McMillan, N.J., McManus, C.E., & Daniel, D.L. (2015) Provenance determination of sapphires and rubies using Laser-Induced Breakdown Spectroscopy and multivariate analysis. American Mineralogist, 100, 1921–1931.Google Scholar
Labutin, T.A., Lednev, V.N., Ilyin, A.A., & Popov, A.M. (2016) Femtosecond Laser-Induced Breakdown Spectroscopy. Journal of Analytical Atomic Spedctrometry, 31, 90–118.CrossRefGoogle Scholar
Lasue, J., Wiens, R., Clegg, S., et al. (2012) Remote Laser‐Induced Breakdown Spectroscopy (LIBS) for lunar exploration. Journal of Geophysical Research, 117, DOI:10.1029/2011JE003898.CrossRefGoogle Scholar
Lasue, J., Clegg, S.M., Forni, O., et al. (2016) Observation of >5 wt % zinc at the Kimberley outcrop, Gale crater, Mars. Journal of Geophysical Research, 121, 338–352.CrossRefGoogle Scholar
Leardi, R. & Gonzalez, A.L. (1998) Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemometrics and Intelligent Laboratory Systems, 41, 195–207.Google Scholar
MacKay, D.J.C. (2003) Information theory, inference, and learning algorithms. Cambridge University Press, Cambridge.Google Scholar
Maurice, S., Wiens, R., Saccoccio, M., et al. (2012) The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) rover: Science objectives and mast unit description. Space Science Reviews, 170, 95–166.CrossRefGoogle Scholar
Melikechi, N., Mezzacappa, A., Cousin, A., et al. (2014) Correcting for variable laser-target distances of Laser-Induced Breakdown Spectroscopy measurements with ChemCam using emission lines of martian dust spectra. Spectrochimica Acta B: Atomic Spectroscopy, 96, 51–60.CrossRefGoogle Scholar
Mezzacappa, A., Melikechi, N., Cousin, A., et al. (2016) Application of distance correction to ChemCam Laser-Induced Breakdown Spectroscopy measurements. Spectrochimica Acta B: Atomic Spectroscopy, 120, 19–29.CrossRefGoogle Scholar
Miziolek, A.W., Palleschi, V., & Schechter, I. (2006) Laser-Induced Breakdown Spectroscopy (LIBS): Fundamentals and applications. Cambridge University Press, Cambridge.Google Scholar
Ollila, A.M., Newsom, H.E., Clark, B., et al. (2014) Trace element geochemistry (Li, Ba, Sr, and Rb) using Curiosity’s ChemCam: Early results for Gale crater from Bradbury Landing Site to Rocknest. Journal of Geophysical Research, 119, 255–285.Google Scholar
Pokrajac, D., Lazarevic, A., Kecman, V., et al. (2014) Automatic classification of Laser-Induced Breakdown Spectroscopy (LIBS) data of protein biomarker solutions. Applied Spectroscopy, 68, 1067–1075.CrossRefGoogle ScholarPubMed
Radziemski, L.J., Loree, T.R., & Cremers, D.A. (1983) Laser-Induced Breakdown Spectroscopy (LIBS): A new spectrochemical technique. In: Optical and laser remote sensing (Killinger, D.K. & Mooradian, A., eds.). Springer-Verlag, Berlin and Heidelberg, 303–307.CrossRefGoogle Scholar
Rosipal, R. & Krämer, N. (2006) Overview and recent advances in partial least squares. In: Subspace, latent structure and feature selection (Saunders, C., Grobelnik, M., Gunn, S., & Shawe-Taylor, J., eds.). SLSFS 2005. Lecture Notes in Computer Science, 3940. Springer-Verlag, Berlin and Heidelberg, 34–51.Google Scholar
Russo, R.E., Mao, X.L., Bol’shakov, A.A., & Yoo, J. (2012) Real-time elemental and isotopic analysis at atmospheric pressure in a laser ablation plasma. Goldschmidt, 76, 2308.Google Scholar
Sarle, W.S. (1994) Neural networks and statistical models. Proceedings of the 19th Annual SAS Users Group International Conference, 1538–1550.Google Scholar
Schlenke, J., Hildebrand, L., Moros, J., & Laserna, J.J. (2012) Adaptive approach for variable noise suppression on Laser-Induced Breakdown Spectroscopy responses using stationary wavelet transform. Analytica Chimica Acta, 754, 8–19.CrossRefGoogle ScholarPubMed
Schröder, S., Meslin, P.-Y., Gasnault, O., et al. (2015) Hydrogen detection with ChemCam at Gale crater. Icarus, 249, 43–61.CrossRefGoogle Scholar
Shenk, J.S., Westerhaus, M.O., & Berzaghi, P. (1997) Investigation of a LOCAL calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy, 5, 223–232.Google Scholar
Shi, Q., Niu, G., Lin, Q., Xu, T., Li, F., & Duan, Y. (2015) Quantitative analysis of sedimentary rocks using Laser-Induced Breakdown Spectroscopy: Comparison of support vector regression and partial least squares regression chemometric methods. Journal of Analytical Atomic Spectrometry, 30, 2384–2393.CrossRefGoogle Scholar
Singh, J.P. & Thakur, S. (2007) Laser-Induced Breakdown Spectroscopy. Elsevier, Philadelphia.Google ScholarPubMed
Sirven, J.B., Bousquet, B., Canioni, L., & Sarger, L. (2006) Laser-Induced Breakdown Spectroscopy of composite samples: Comparison of advanced chemometrics methods. Analytical Chemistry, 78, 1462–1469.Google Scholar
Sivakumar, P., Taleh, L., Markushin, Y., Melikechi, N., & Lasue, J. (2013) An experimental observation of the different behavior of ionic and neutral lines of iron as a function of number density in a binary carbon–iron mixture. Spectrochimica Acta B: Atomic Spectroscopy, 82, 76–82.CrossRefGoogle Scholar
Sivakumar, P., Taleh, L., Markushin, Y., & Melikechi, N. (2014) Packing density effects on the fluctuations of the emission lines in Laser-Induced Breakdown Spectroscopy. Spectrochimica Acta B: Atomic Spectroscopy, 92, 84–89.CrossRefGoogle Scholar
Smola, A.J. & Schölkopf, B. (2004) A tutorial on support vector regression. Statistics and Computing, 14, 199–222.Google Scholar
Sneath, P.H. & Sokal, R.R. (1973) Numerical taxonomy: The principles and practice of numerical classification. W.H. Freeman, New York.Google Scholar
Starck, J.L., Pantin, E., & Murtagh, F. (2002) Deconvolution in astronomy: A review. Publications of the Astronomical Society of the Pacific, 114, 1051.Google Scholar
Tognoni, E., Cristoforetti, G., Legnaioli, S., & Palleschi, V. (2010) Calibration-Free Laser-Induced Breakdown Spectroscopy: State of the art. Spectrochimica Acta B: Atomic Spectroscopy, 65, 1–14.CrossRefGoogle Scholar
Tucker, J., Dyar, M., Schaefer, M., Clegg, S., & Wiens, R. (2010) Optimization of Laser-Induced Breakdown Spectroscopy for rapid geochemical analysis. Chemical Geology, 277, 137–148.Google Scholar
Vance, T., Pokrajac, D., Lazarevic, A., et al. (2010) Classification of LIBS protein spectra using multilayer perceptrons. Transactions on Mass-Data Analysis of Images and Signals, 2, 96–111.Google Scholar
Wang, Y., Lysaght, M.J., & Kowalski, B.R. (1992) Improvement of multivariate calibration through instrument standardization. Analytical Chemistry, 64, 562–564.Google Scholar
Ward, J.H. Jr. (1963) Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.CrossRefGoogle Scholar
Wiens, R., Maurice, S., Barraclough, B., et al. (2012) The ChemCam Instrument Suite on the Mars Science Laboratory (MSL) rover: Body unit and combined system tests. Space Science Reviews, 170, 167–227.CrossRefGoogle Scholar
Wiens, R.C., Maurice, S., Lasue, J., et al. (2013) Pre-flight calibration and initial data processing for the ChemCam Laser-Induced Breakdown Spectroscopy instrument on the Mars Science Laboratory Rover. Spectrochimica Acta B: Atomic Spectroscopy, 82, 1–27.Google Scholar
Wisbrun, R., Schechter, I., Niessner, R., Schroeder, H., & Kompa, K.L. (1994) Detector for trace elemental analysis of solid environmental samples by laser plasma spectroscopy. Analytical Chemistry, 66, 2964–2975.CrossRefGoogle Scholar
Wold, S. & Sjöström, M. (1977) Method for analyzing chemical data in terms of similarity and analogy. Chemometrics: Theory and application. (Kowalski, B.R., ed.). ACS Symposium Series. American Chemical Society, Washington, DC, 243–282.Google Scholar
Wold, S., Sjöström, M., & Eriksson, L. (2001) PLS-regression: A basic tool of chemometrics. In: Chemometrics and Intelligent Laboratory Systems, 58, 109–130.Google Scholar
Yaroshchyk, P., Death, D., & Spencer, S. (2012) Comparison of principal components regression, partial least squares regression, multi-block partial least squares regression, and serial partial least squares regression algorithms for the analysis of Fe in iron ore using LIBS. Journal of Analytical Atomic Spectrometry, 27, 92–98.Google Scholar
Zhang, B., Sun, L., Yu, H., Xin, Y., & Cong, Z. (2013) Wavelet denoising method for Laser-Induced Breakdown Spectroscopy. Journal of Analytical Atomic Spectrometry, 28, 1884–1893.Google Scholar
Zhang, P. (1993) Model selection via multifold cross validation. The Annals of Statistics, 21(1), 299–313.Google Scholar

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