The data processing strategies used for the retrospective analysis of the archived data mainly consist of typical NTA workflows. There have been several extensive reviews on such workflows and all the included steps. Once these data are processed and further annotated, the identified signals are aligned over multiple datasets for trend analysis and/or inference. 105-107 However, the confidence levels associated with those identification may not be the same across different samples. Depending on the quality of the generated data (e.g. signal to noise ratio) those identifications may be less reliable that others. In addition to that, the unidentified signals cannot be aligned unless generated under the same experimental conditions. These challenges have resulted in several attempts in assessing the data quality as well as the chromatogram alignment. The quality of the collected data for NTA assays defines whether the generated signals can be confidently identified or not. The quality of the acquired data may be compromised due to heavy matrix effects, instrumental issues, and/or the nature of the chemical itself. For some chemicals to generate an HRMS signal of acceptable quality, they must be analyzed using specific conditions. These issues with the data quality can be only detected once the data goes through complete data processing workflow. A common approach to approximately assess the quality of the collected data is to look for the added internal standards in the samples. This approach, even though effective, is computationally expensive and requires detailed metadata about the experimental setup (e.g. the separation selectivity, ionization efficiency, data acquistion conditions). Such information may or may not be available for a specific study, depending on the objectives. Moreover, depending on the number and the spread of internal standards, they may not be enough for assessing the quality of the collected data. Some efforts have been put towards standardizing the NTA data generation procedures. However, the proposed procedures are specific to different communities and do not translate across. As an example, the metabolomics and exposomics communities use different retention index scales (alkylamide 113 vs University of Athens scale ), making comparison of such datasets extremely difficult. Furthermore, these measures tend to be overly conservative resulting in low levels of implementation within each community.