Session: Parallel session 9 - Complex mixtures - polymers - microplastics

Untargeted and automatic FT-ICR Mass Spectrometry Data Processing of Complex Mixtures from Mass Differences analyses

Germain SALVATO VALLVERDU^1,2, Ryan P RODGERS^2,3, Martha L CHACON-PATIÑO^2,3, Christopher RÜGER⁶, Christopher HOLDER MONTENEGRO^2,3, Julien MAILLARD^2,4, Carlos AFONSO^2,5, Christopher HENDRICKSON³, Brice BOUYSSIERE^1,2, Pierre GIUSTI^2,4

¹Université de Pau et des Pays de l'Adour, Pau, France
²International Joint Laboratory - iC2MC : Complex Matrices Molecular Characterization, Harfleur, France
³National High Magnetic Field Laboratory, Tallahassee, Florida, United States
⁴TotalEnergies, Harfleur, France
⁵COBRA - University of Rouen-Normandy, Rouen, France
⁶University of Rostock, Rostock, Germany

Recent advances in instrumentation have increased mass spectral performance metrics and expanded complex mixture applications. However, complex mixture data processing methods have lagged the advanced pace of analytical capabilities, which has created a bottleneck in the overall workflow. Herein, a fully automated data processing workflow applicable to a variety of complex matrices is described (dissolved organic matter, biofuels, Li-ion batteries, and emerging contaminants). The method relies on extremely accurate mass differences, to create elemental composition differences and molecular assignments.

All samples were characterized by online electrospray ionization with 21 or 12-tesla FT-ICR MS. Our data processing is implemented in an open-source, Python-based, software platform, PyC2MC.

All analyzed samples contain abundant, repeating mass differences, linked to their structures or chosen synthetic pathways for chemical production / degradation patterns. The mass differences are calculated and the number of occurrences of each mass difference is computed, along with the center and width at half height. These values are compared against a mass difference database collected from HRMS data. The most abundant match (or a collection of matches) is then used to recalibrate the entire spectrum using the Ledford equation and theoretical mass differences values, which enables accurate recalibration of the mass spectrum (< 100 ppb rms error) without internal calibrants. Elemental constraints for molecular formula assignment are determined similarly, through a match to the isotopic database. Finally, the relationship between precision, S/N, and resolving power sets the allowable error window for molecular formula assignment based on a user defined confidence.

Mass difference analysis enables recalibration and assignment of complex mass spectra without the need for internal calibrants

Work supported by the NSF through DMR-2128556 and DMR-1644779, and the State of Florida.