This paper focuses on data arising from the field of metabolomics, a rapidly developing area concerned by the analysis of the chemical fingerprints (i.e. the metabolite profile). The metabolite profile is left by specific chemical processes occurring in biological cells, tissues or organs. It is the main purpose of this article to develop and implement scoring techniques so as to rank all possible metabolic profiles by increasing order of magnitude of the conditional probability that a given metabolite is present at high levels in a certain biological fluid. After a detailed description of the (functional) data from which decision rules must be learnt, several approaches to this predictive problem, based on recent advances in K-partite ranking are described at length. Their performance on several real datasets are next thoroughly investigated.