Bayesian approach to 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 proposed. 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 proposed approach is studied using synthetic data generated on the basis of some real-world reference spectra. The results of study are compared with those obtained by means of the currently used method for estimation of concentrations, viz. constrained least-squares curve fitting.