There is certainly a “symphony” of data analysis strategies and approaches throughout the pipeline, from post-acquisition steps to the variable selection. Algorithms or methods used at each step of the pipeline affect the downstream analyses and outcome . In this study, using the same LC-MS raw data, but changing the pre-processing parameters (Section 3.1) and data pre-treatment methods (Section 3.2) affected the downstream statistical outcome, thus illustrating (to a certain extent) not only that the metabolomic data are indeed information-rich, but also the limitation in existing chemometric methods and the need of uttermost care in data handling. Furthermore, the results demonstrate that, currently, the possible “Archimedean” methodical point for an optimal extraction of information from untargeted metabolomic datasets could be the exploration of existing chemometric and statistical methods at each step of data analysis pipeline.