Other
Scientific paper
Sep 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008dps....40.3214d&link_type=abstract
American Astronomical Society, DPS meeting #40, #32.14; Bulletin of the American Astronomical Society, Vol. 40, p.453
Other
Scientific paper
Laser-induced breakdown spectrometers (LIBS) will be included on both the ChemCam instrument on the Mars Science Laboratory and the Raman-LIBS instrument on ExoMars. Because LIBS spectra are especially sensitive to chemical matrix effects, extracting quantitative elemental concentrations of complex geochemical samples presents a challenge. We are using igneous and metamorphosed rocks to test three methods for calibration of LIBS spectra.
1) The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. Statistical analysis can identify which peaks have areas that are most closely proportional to concentration for each element in rocks types likely to be found on Mars. Several normalization techniques have been employed to compensate for the chemical matrix effects with only modest success.
2) Partial least squares analysis (PLS) generates a regression model that correlates two matrices: the LIBS spectra (a Y matrix containing the elemental concentrations for each element in each calibration sample) and the intensities (an X matrix including the intensity of each channel for each calibration sample. These are described by Y = X B, where the B matrix describes the relationship between the pixel intensities and the elemental response.
3) Step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element develops a multivariate expression to predict elemental concentrations on the basis of the areas of a small number of peaks (4-13 for each element).
It is likely that successful quantification of LIBS data for Mars missions will result from some combination of these approaches. Future work will apply them to a broader range of rock types and elemental concentrations, and explore other multivariate methods.
Barefield E. II J.
Clegg Steve
Dyar Melinda
Schaefer Martha W.
Tucker Jordan
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