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PreprintCC BY-NC-NDChemRxiv

Revisiting Artifact Suppression in Electrospray Ionization Deconvolution

  • Michael T. Marty1,2
  1. 1Department of Chemistry
  2. 2University of Texas at Austin
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Abstract

Charge state assignment is a critical step in many workflows for intact protein analysis by mass spectrometry. Computational deconvolution algorithms are increasingly used to automate data analysis, but conventional approaches can present artifacts. Here, we explore several approaches to remove artifacts by limiting the charge state assignments for each data point. One approach removes satellite charge states surrounding local maxima. Another removes harmonics at double a more probable charge assignment. The last two limit either the total number of allowed charge state assignments or the minimum relative charge state assignment probability for a given data point. These new features are as effective as the prior SoftMax artifact removal in UniDec, but they enable easier tuning by the user. Although the new set of features introduces additional complexity and an expanded number of parameters, these tools enable users to more precisely reduce artifacts in deconvolution results.

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Cite

BibTeXRIS
BibTeX
@misc{marty2026revisiting,
  title = {{Revisiting Artifact Suppression in Electrospray Ionization Deconvolution}},
  author = {Michael T. Marty},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15009546/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009546/v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Revisiting Artifact Suppression in Electrospray Ionization Deconvolution
AU  - Michael T. Marty
PY  - 2026
DA  - 2026/09/28
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15009546/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009546/v1
AB  - Charge state assignment is a critical step in many workflows for intact protein analysis by mass spectrometry. Computational deconvolution algorithms are increasingly used to automate data analysis, but conventional approaches can present artifacts. Here, we explore several approaches to remove artifacts by limiting the charge state assignments for each data point. One approach removes satellite charge states surrounding local maxima. Another removes harmonics at double a more probable charge assignment. The last two limit either the total number of allowed charge state assignments or the minimum relative charge state assignment probability for a given data point. These new features are as effective as the prior SoftMax artifact removal in UniDec, but they enable easier tuning by the user. Although the new set of features introduces additional complexity and an expanded number of parameters, these tools enable users to more precisely reduce artifacts in deconvolution results.
ER  -