The last step of NTA assays consist of the identification workflows, including spectral matching against the experimental or in-silico predicted spectra. Independently from the source of the reference spectra, i.e. experimental or predicted, the quality of the measured spectrum to be identified is essential. In the field of exposomics, the efforts in assessing the quality of the recorded spectra is limited to a recent study with limited applicability. In addition to the spectral quality, the spectral matching algorithms also play an important role during the identification process. These algorithms range from simple dot product to spectral entropy and more data driven approaches (e.g. deep learning). For the spectral library matching the size of the spectral library and its coverage of the relevant chemical space is an essential factor. For example, merging all open and commercial libraries together only 40 % the metabolic networks is covered. As for in-silico fragmentation tools, access to large and relevant chemical subspace is important. These approaches employ the structure of the different candidates retrieved from the chemical databases for the fragmentation or fingerprint prediction. However, these approaches, even though powerful, have limitations due to the limited coverage of the exposome chemical space by current chemical databases.