For further validation, the workflow was benchmarked for feature detection, quantification and marker selection using the publicly available Thermo Q Exactive dataset with the MetaboLights accession MTBLS733 that includes two standard mixtures (SA, SB) obtained from Piper nigrum extracts with 5 replicates per mixture . Each mixture consists of the same compounds, some of which are in different concentrations. The concentration ratios between the two mixtures define different compound groups (Gm, Gd1-Gd6), as previously described by Zhucui Li et al. . In the published research related to the dataset the authors performed targeted analysis using vendor software (refer to the relevant publication for further details) and identified 836 unique features, a number that represents the maximal number of features that can be detected with untargeted software packages and evaluated four untargeted metabolomics processing software (MS-Dial, MZmine 2, XCMS and Compound Discoverer) for feature detection, quantification, and marker selection. To evaluate the software performance quantitatively, all compound-derived true feature fold-changes (SB:SA) were calculated with targeted analysis . Following the author's directions, and after parameter optimization (Table ), UmetaFlow could detect 778 true features, a 93.1% untargeted versus targeted identification rate. Out of all true features detected, 736 were accurately quantified (94.6%). To assess the quantification accuracy of UmetaFlow, the foldchanges of the intensities between the mixtures SA and SB of all true features identified were calculated. Then, those fold-changes (FC) were log-transformed and plotted for comparison of the targeted and untargeted approach and the results indicated high accuracy and low variation between features of the same group (Figure ). The dataset included 50 discriminating markers with p values < 0.05 and fold-changes < 0.5 or >2. UmetaFlow could detect 47 true and only 5 false discriminating markers. The performance of the workflow is significantly enhanced with the re-quantification step, detecting 20 additional true features, as well as detecting 2 more true discriminating markers than if we omit this step (Figure ; Table ).