Considerable literature exists on the data analysis and mathematical description of algorithms and chemometric methods used during data analysis in metabolomics, with suggestions and requirements for a sound approach . However, questions such as (i) to what extent can an untargeted metabolomic dataset be mined; (ii) are the data analysis methodologies, applied to an untargeted metabolomic dataset, not biased by the scope of the initial biologic question; (iii) are the current chemometric tools and algorithms fit to holistically extract information from the mega-variate datasets generated by untargeted metabolomics studies ; (iv) to what extent do the different steps in a data handling methodology pipeline influence the data analysis output; and (v) what could be a methodological approach and practice that would aid to maximize the value of untargeted metabolomic dataset all remain to be explored.