Untargeted LC-MS metabolomic analyses generally generate a wealth of data. The data structure is a two-way matrix (retention time and mass spectra directions), with thousands of data entries, depending on the complexity of the extracts, per sample. To make it more complicated, the data inherently contain vast amounts of noise, artifacts, unintended fragments and adducts, potentially making components of the datasets either not usable or redundant . The challenge is how to extract and create a “clean” dataset from such raw data in a way that captures as much usable information as possible for downstream pattern recognition, classification, and feature selection. Most pre-processing software pipelines share the general functions of peak detection-, alignment, and annotation. Currently, several open-source, as well as commercial software programs, have been developed to aid in metabolomics data processing (up to metabolite annotation for some). Each of these tools has its own capabilities, providing some context-dependent insights, but also limitations . The detailed description of all of these tools and algorithms is beyond the scope of this paper and the reader is referred to the cited literature.