Algorithms for estimation of concentrations in spectrophotometric analysis of multicomponent substances

The spectrophotometric analysis of a chemical substance is based on the interpretation of the measurement data acquired by means of a spectrophotometer, i.e., on estimation of the concentrations of its components. In this paper, a Bayesian approach to the estimation of those concentrations is compared with a more traditional approach based on selection and deconvolution. Its effective application requires a considerable amount of statistical a priori information, viz., the probability density functions characterizing the distributions of the concentrations, of the errors in the data, and of the residual components in the analyzed substance whose concentrations are not estimated. The compared methods of estimation of concentrations are studied and compared using some real-world spectrophotometric data. The results of the study are finally compared with those obtained by means of the currently used method for estimation of concentrations, viz., constrained least-squares curve fitting.