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<p>Analytical techniques such as thermal ionization mass spectrometry are routinely employed to characterize the isotopic composition of diverse natural and anthropogenic materials. The use of Bayesian inference, a data-driven statistical process, may offer value in mass spectrometric metrology. In this contribution, we review and suggest the implementation of Markov chain Monte Carlo algorithms to better quantify isotope ratios in support of high-precision mass spectrometry data processing. In doing so, we aim to present a relatively underutilized statistical approach to the mass spectrometric community and emphasize how practitioners can seamlessly implement Bayesian analysis into their own research.</p>

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Bayesian statistical analysis for mass spectrometric data processing

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Ellis McLarty,
  • Alexis T. Riche,
  • Kyle M. Samperton,
  • Elizabeth LaBone,
  • Shelby Bowden

摘要

Analytical techniques such as thermal ionization mass spectrometry are routinely employed to characterize the isotopic composition of diverse natural and anthropogenic materials. The use of Bayesian inference, a data-driven statistical process, may offer value in mass spectrometric metrology. In this contribution, we review and suggest the implementation of Markov chain Monte Carlo algorithms to better quantify isotope ratios in support of high-precision mass spectrometry data processing. In doing so, we aim to present a relatively underutilized statistical approach to the mass spectrometric community and emphasize how practitioners can seamlessly implement Bayesian analysis into their own research.