In atomic mass spectrometry, polyatomic interferences that obscure ions of interest are a serious concern and in many cases hamper, or make impossible, the measurement of low signal analytes. Electron multipliers in pulse counting mode are used prolifically when low intensity ion beams are present during analysis as they provide on the order of 106-107 signal amplification, however simply counting the number of electron pulses ignores differences in the impact physics that are present depending on ion identity. The objective of this research was to investigate the efficacy of utilizing advanced neural network analysis and machine learning to recognize the differences in the analog electron multiplier pulse shapes for atomic vs. polyatomic ions of similar or identical mass as a means of distinguishing the two ion types.
In this work, we’ve successfully discriminated polyatomic interferants from atomic analytes of interest for two different mass spectrometers, and three different ion pairs; Krypton isotope/hydride and methane/oxygen (noble gas mass spectrometer) and 129I atomic vs 129I polyatomic (Triton mass spectrometer). Discrimination accuracy for these experiments ranged from 76% to 95%.