Mass Spectral Prediction Algorithms. These analyses used NEIMS (version 1.0) to calculate the mass spectral profiles for monosubstituted α-amino acids. Instructions on installation and setup of NEIMS can be found at . We used the NEIMS pretrained machine learning model weights trained on nearly 300,000 EI-MS spectra within the NIST MS 2017 database. Other prediction algorithms do exist in the literature for simulating EI-MS spectra, however we focus our efforts on NEIMS for numerous reasons. CFM-EI has depreciated support for electron ionization prediction in the latest version (CFM-ID 4.0) and our efforts failed to compile previous versions published by developers despite troubleshooting efforts. RASSP was not included in this study due to repeated compilation failures across tested environments, specifically with errors commonly encountered by other developers (e.g. see ) pertaining to the RASSP environment file. Despite extensive efforts to resolve these issues, including manually installing dependencies, RASSP could not be successfully implemented for evaluation. QCxMS Libraries of GC-EI-MS Spectra. We investigated three libraries for this study (i) the spectra found within the NEIMS training set (NIST17) (ii) an open source repository filtered to remove duplicate spectra from NIST17 -MoNA f and (iii) the IOCB hand-curated dataset. As previously mentioned, the NEIMS neural network was trained on ~300,000 EI-MS spectra present in the NIST 2017 database. Of these ~300,000 spectra, monosubstituted α-amino acids comprise only ~0.01% of the training data.