The data processing and statistical modeling are crucial and vital, as the information extracted from the raw data will depend on these steps. A better understanding of data processing and statistical algorithms and methods are important to achieve statistically-relevant and optimal biological information. These post-acquisition steps can be challenging and time-consuming and comprise data cleaning and generation of statistical models that are explorative and predictive. Certainly, different parameters and algorithms used in these data analyses steps would lead to different outputs and influence the extent of data mining outcomes . This observation, thus, raises a series of questions of knowing how to handle untargeted metabolomic data adequately, or if there is one single “formula” or methodological protocol to follow for maximal exploration of untargeted metabolomic datasets.