| Abstract | Laser-induced breakdown spectroscopy (LIBS) was used to quantitatively analyze 195 rock slab samples
with known bulk chemical compositions, 90 pressed-powder samples derived from a subset of those
rocks, and 31 pressed-powder geostandards under conditions that simulate the ChemCam instrument
on the Mars Science Laboratory Rover (MSL), Curiosity. The low-volatile (<2 wt.%) silicate samples (90
rock slabs, corresponding powders, and 22 geostandards) were split into training, validation, and test
sets. The LIBS spectra and chemical compositions of the training set were used with three multivariate
methods to predict the chemical compositions of the test set. The methods were partial least squares
(PLS), multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs.
Both the full LIBS spectrum and the intensity at five pre-selected spectral channels per major element
(feature selection) were used as input data for the multivariate calculations. The training spectra were
supplied to the algorithms without averaging (i.e. five spectra per target) and with averaging (i.e. all spectra
from the same target averaged and treated as one spectrum). In most cases neural networks did not
perform better than PLS for our samples. PLS2 without spectral averaging outperformed all other procedures
on the basis of lowest quadrature root mean squared error (RMSE) for both the full test set and the
igneous rocks test set. The RMSE for PLS2 using the igneous rock slab test set is: 3.07 wt.% SiO2, 0.87 wt.%
TiO2, 2.36 wt.% Al2O3, 2.20 wt.% Fe2O3, 0.08 wt.% MnO, 1.74 wt.% MgO, 1.14 wt.% CaO, 0.85 wt.% Na2O,
0.81 wt.% K2O. PLS1 with feature selection and averaging had a higher quadrature RMSE than PLS2, but
merits further investigation as a method of reducing data volume and computation time and potentially
improving prediction accuracy, particularly for samples that differ significantly from the training set. Precision
and accuracy were influenced by the ratio of laser beam diameter (490 lm) to grain size, with
coarse-grained rocks often resulting in lower accuracy and precision than analyses of fine-grained rocks
and powders. The number of analysis spots that were normally required to produce a chemical analysis
within one standard deviation of the true bulk composition ranged from 10 for fine-grained rocks to >20
for some coarse-grained rocks.
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