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Laser-induced breakdown spectroscopy (LIBS) is an analytical technique real-time geochemical analysis that is being developed for portable use outside of the laboratory. In this study, statistical signal processing and classification techniques were applied to single-shot, broadband LIBS spectra, comprising measured plasma light intensities between 200 and 960 nm, for a suite of 157 garnets of different composition from 92 locations worldwide. Partial least squares discriminant analysis was applied to sets of 25 LIBS spectra for each garnet sample and used to classify the garnet samples based on composition and geographic origin. Careful consideration was given to the cross-validation procedure to ensure that the classification algorithm is robust to unseen data. The results indicate that broadband LIBS analysis can be used to discriminate garnets of different composition and has the potential to discern geographic origin. © 2010

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The wide use of psychometric assessments and the time necessary to conduct comprehensive psychometric tests has motivated significant research into the development of psychometric testing procedures that will provide accurate and efficient estimates of the parameters of interest. One potential framework for developing adaptive psychometric procedures is the Theory of Optimal Experiments. The Theory of Optimal Experiments provides several metrics for determining informative stimulus values based on a model of the psychometric function to be provided by the investigator. In this study, two methods based on a previous implementation of the Theory of Optimal Experiments are presented for comparison to two fixed step size staircase methods and also an existing adaptive method that utilizes a Bayesian framework. The psychometric procedures were used to measure detection thresholds and discrimination limens on two separate psychoacoustic tasks with normal-hearing subjects. Computer simulations were performed based on the outcomes of the experimental psychoacoustic detection task to analyze performance over a large sample size in the case of known truth. Results suggest that the proposed stimulus selection rules motivated by the Theory of Optimal Experiments perform better than previous techniques and also extend estimation to multiple parameters.