Quantum chemistry based prediction of electron ionization mass spectra for environmental chemicals

There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semi-automated workflow for the in silico prediction of electron ionization high-resolution mass spectra based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals and accuracy evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows. The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.