Nonparametric Bayesian inference on environmental waters chromatographic profiles

Chromatographic signals have a specific microscopic behaviour which enables to statistically model the retention time of molecules. Such microscopic point of view is adopted in this paper for addressing the inverse problem of chromatographic profiles inference in a Nonparametric Bayesian framework in order to propose an automatic unsupervised alternative to the traditional chemometrics methods. An application on inference on the concentration of micropollutants in lake water highlights the relevance of this approach when the studied mixture contains an unknown number of components.