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A high resolution Fourier transform ion trap enabled by image current splicing: a theoretical study.

Haoqiang YanDayu LiWei Xu
Published in: Analytical methods : advancing methods and applications (2023)
The relatively high work pressure within an ion trap has limited the implementation of the Fourier transform technique for high resolution mass analysis. The main reason is that high buffer gas pressure will cause the rapid decay of ion oscillations. In this study, an image current splicing method based on the filter diagonalization method (FDM) and the Hilbert transform was developed to increase the resolving power of nondestructive mass analysis in a linear ion trap. First, multiple repeated experiments (or ion trajectory simulations) were performed to collect multiple sets of data. Using the FDM, the frequency component distribution was extracted from short image current transients collected from each experiment. The Hilbert transform was then applied to calculate and normalize the decay envelope of each transient. The relative abundance was calculated by counting the envelopes. Finally, image current transients collected from these multiple experiments were spliced and merged into a whole signal with much longer duration and continuous phase. This splicing method could effectively increase the duration of the image current, and thus improve the mass resolution of the ion trap mass analyzer. The mass resolution ( m /Δ m ) was improved from 183.5 to 5.8 × 10 3 , and the average relative difference was 2.8%. The proposed method resolved 3 adjacent peaks which originally could not be resolved from the raw signal by the fast Fourier transform (FFT). Besides simulated data, this method was also applied to the experimental data collected from a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. The influence of electronic noise on the proposed method was also discussed in this study.
Keyphrases
  • high resolution
  • deep learning
  • electronic health record
  • mass spectrometry
  • air pollution
  • single molecule
  • microbial community
  • artificial intelligence
  • brain injury
  • ionic liquid