In this work, a discussed and step-wise tutorial for LC-MS and GC-MS data processing using the open-access software MZMine2 is presented and discussed. The rationale behind each step was demonstrated to enable the readers to go through their own data and process it accordingly. The main lesson to be learned is that each parameter must be chosen in light of the raw data and no guidelines should suggest a predetermined value. Still, it is worth mentioning that ideal values for each parameter do not exist, and that the user might end up investing too much time futilely optimizing values. Our suggestion is to process your data in light of the raw data (and the study design) following the preview figure result and the resulting feature list generated in each processing step, interpret your data, and go back to process it again to tune the detection of important features.

The metabolomics approach is widespread and accepted in several lines within life sciences. However, much of what has been called metabolomics could be better defined as a comparison study, regardless of its purpose. Metabolomics finds its definition in the understanding of biological properties and effects on a given organism. In any case, the tools used in metabolomics are undoubtfully useful and should be used in natural products studies, both those already established and those that are under development. Natural products research is in a continuous process of evolution to become a discipline driven by multivariate data with a solid influence of “omic” technologies.