Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Abstract

Organic pollutants can be identified by comparing their electron ionization (EI) mass spectra with those in libraries or obtained from authentic standards. Nevertheless, libraries are incomplete; standards may be unavailable or too costly, or their synthesis may be too time-consuming. This study evaluates the performance of quantum chemical electron ionization mass spectrometry (QCEIMS) vis-à-vis competitive fragmentation modeling (CFM) for suspect screening and unknown identification. EI mass spectra of 35 compounds, including halogenated organics, organophosphorus flame retardants (OPFRs), and disinfection byproducts were computed. Computational results were compared with EI mass spectra compiled in the NIST Library as well as collision-induced dissociation (CID) mass spectra obtained from radical cations M•+ generated by charge-exchange atmospheric pressure chemical ionization (APCI). The results indicate that QCEIMS performs equivalently or better than CFM. Average match factors between computed and experimental (NIST) EI mass spectra were 656 vs 503 for the halogenated organics, and on average, QCEIMS predicted 55% of the products generated by CID vs 17% predicted by CFM. QCEIMS predicted 37% of the OPFR CID products whereas CFM predicted 29%. QCEIMS performed comparably to a commercial combinatorial fragmentation method for suspect screening of a dust sample, identifying 19/20 targets. Examples of unknown pollutants, whose reference spectra were unavailable at the time of discovery, are also presented. The computational results suggest that QCEIMS can help guide the analyst in obtaining authentic standards and raise the possibility that, with advances in computing, an unknown may eventually be confirmed in hours as opposed to the days or months required to obtain authentic standards.

From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics

“…It was shown that the program is able to identify semiochemicals and metabolites of prior unknown compounds better than the commonly used CFM-ID program. 30,31 As an alternative to EI, the ionization of molecules can be obtained through protonation. Since the nature of the primary ions between these approaches differs (usually an open-shell vs closed-shell electronic structure), the underlying potential energy surfaces (PESs) are (at least initially) different as well.…”

“…The program provides reasonable EI mass spectra for organic, inorganic, and transition-metal-containing molecules that agree well with experimental spectra. − Because the method is based on MD simulations, the resulting trajectories provide detailed insights into fragmentation processes and rearrangement reactions that are difficult to derive otherwise. It was shown that the program is able to identify semiochemicals and metabolites of prior unknown compounds better than the commonly used CFM-ID program. , …”

An Actionable Annotation Scoring Framework for Gas Chromatography - High Resolution Mass Spectrometry (GCHRMS)

“…For the discovery of unknown unknowns where no libraries or standards exist, the in-silico prediction of EI spectra (Allen et al, 2016;Spackman et al, 2018;Wang et al, 2020;Wei et al, 2019) is more straight forward than collision-induced dissociation (CID) (Koopman and Grimme, 2021) and higher energy C-trap dissociation (HCD) spectra used for LC-HRMS analysis. Although, it is important to recognize that false positive rates vary across all in silico approaches (Schreckenbach et al, 2021). In addition to predicted spectral matching, EI spectra can be used for molecular networks and substructure characterization (e.g., (Elie et al, 2019;Hummel et al, 2010;Lai et al, 2018;Stein, 1995)).…”

Nontargeted Screening Using Gas Chromatography–Atmospheric Pressure Ionization Mass Spectrometry: Recent Trends and Emerging Potential

“…CSI: Finger ID was used by Larson et al to identify products of lignin pyrolysis [42]. QCEIMS was evaluated by Schreckenbach et al [110] to predict the mass spectra of selected halogenated and organophosphorus flame retardants. While QCEIMS is designed for EI spectral prediction, it can also be informative when predicting the CID mass spectra of ions M •+ generated by charge exchange (GC-APCI) or photoionization (GC-APPI).…”