Multicomponent spectral fitting

Other multivariate methods have been applied to ICP spectra for quantitative measurements. As examples, they include multicomponent spectral fitting (which is incorporated in several commercial instrument software) [81] matrix projection, which avoids measurement of background species [94,95] generalised standard additions [96] and Bayesian analysis [97]. 

The multicomponent spectral fitting approach has been the most successful for a broad range of sample types [ 153,154]. A linear combination of spectra of all of the individual components (elemental ions and polyatomic ions) is optimized for best fit of the experimental spectrum. Model spectra are determined from the natural abundances of the elements and polyatomic model spectra are calculated from the isotopic abundances of the individual element isotopes. Therefore, experimental model spectra are not required. 

Statistical information derived from spectral averaging represents an additional benefit. Calculation of standard deviation for each data point can be used to detect bad results or to select the optimum wavelength for a single-component analysis. Statistic information is also of particular importance in curve-fitting multicomponent analysis. 

Multiwavelength methods. Least squares curve fitting techniques may be used in the determination of multicomponent mixtures with overlapping spectral features. Two classical quantitation methods, the Classical Least Squares (CLS) mode and the Inverse Least Squares (ILS) model, are applied when wavelength selection is not a problem. CLS is based on Beer s law and uses large regions of the spec-tram for calibration but cannot cope with mixtures of interacting constituents. ILS (multivariate method) can accurately build models for complex mixtures when only some of the constituent concentrations are known. 

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