Untargeted data acquisition leads to a considerable number of features, which not only complicates data analysis but many features are a result of noise or of non-pertinent analytes. Therefore, different approaches are used to simplify the data sets and extract unknown features of relevance. Here, four separate approaches were tested and evaluated to extract features that may be polyphenolic but were not able to be annotated previously by in silico fragmentation, MS or MS 2 matching. These features are thus labeled as Level 4 if a molecular formula was predicted, and Level 5 if nothing else is known about the feature.