Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements
Colin M. Weber, Dwaipayan Ray, Adrian A. Valverde, Jason A. Clark,, Kumar S. Sharma

TL;DR
This paper demonstrates that Gaussian mixture model clustering algorithms effectively analyze complex PI-ICR mass spectrometry data, improving spot detection accuracy and confirming measurements of specific isotopes.
Contribution
The paper introduces GMM clustering algorithms as an optimal method for analyzing PI-ICR data, outperforming other algorithms in handling noise and non-ideal data scenarios.
Findings
GMM algorithms outperform other clustering methods on simulated PI-ICR data.
GMM-based analysis yields isotope measurements consistent with published values.
GMM clustering improves the accuracy of ion spot detection in complex datasets.
Abstract
The development of the phase-imaging ion-cyclotron resonance (PI-ICR) technique for use in Penning trap mass spectrometry (PTMS) increased the speed and precision with which PTMS experiments can be carried out. In PI-ICR, data sets of the locations of individual ion hits on a detector are created showing how ions cluster together into spots according to their cyclotron frequency. Ideal data sets would consist of a single, 2D-spherical spot with no other noise, but in practice data sets typically contain multiple spots, non-spherical spots, or significant noise, all of which can make determining the locations of spot centers non-trivial. A method for assigning groups of ions to their respective spots and determining the spot centers is therefore essential for further improving precision and confidence in PI-ICR experiments. We present the class of Gaussian mixture model (GMM) clustering…
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