Abstract

This paper describes the use of Bayesian analysis for the deconvolution of ICP-MS spectra, covering the mass range from 46–88 Daltons, derived from multi-element standards and biological reference materials: TORT 1—Lobster Hepatopancreas, NIST 1547—Peach Leaves and NIST 1577b—Bovine Liver. This approach provides information on both the nature of the species that comprise the observed spectrum and the magnitude of their individual contributions. Various tests are applied to determine the goodness of fit between the actual and predicted spectrum, including the statistical evidence, the deviation at each mass and the predicted value for the isotopic abundances, the latter proving to be particularly difficult to rationalise for all elements in the data set. Bayesian deconvolution is not a calibration technique, but the data derived from it are used to produce calibrations and subsequently to carry out quantitative analyses of the reference materials. The algorithm uses the known isotopic distribution patterns in synthesising spectra, but these are corrupted by mass bias introduced by the instrument. Estimates of isotopic mass bias are used to model the instrument response function in order to remove this distortion from the data before it is subjected to processing. The effects of applying such corrections to the data are demonstrated.