It is extremely difficult to objectively compare the available algorithms, as the ground truth is almost impossible to obtain for most NTA data. For example, feature detection algorithms are often compared by investigating the overlap of detected peaks regardless of the peak quality or true positive peaks. While a feature detected by multiple algorithms may be more likely to be true, it is still not certain that the signal is truly coming from a chemical or the background. Moreover, this is often only the first step in the full workflow that can influence outcomes further in the workflow. Therefore, increased flexibility and freedom in data processing workflows and algorithms is needed to extract as much relevant information of the subspace as possible. This is similar to varying the measuring methods of NTA approaches to cover a larger region of the exposome chemical space. To process LC/GC-HRMS data, a variety of tools have been developed including CAM-ERA, DSFP, FOR-IDENT, GNPS, InSpectra, MS-Dial, MZmine, OpenMS, Patroon, Phenomenal, SIRIUS, TidyMass, and XCMS. However, the majority have been developed with the focus on metabolomics applications. The difficulty here is that in NTA a broad range of chemical classes can be found besides metabolites.