Evaluation of Matrix Effects in SIMS Using Gaussian Process Regression: The Case of Olivine Mg Isotope Microanalysis

Mar 31, 2025·
Keita Itano
Keita Itano
,
Kohei Fukuda
,
Noriko Kita
,
Kenta Ueki
,
Tatsu Kuwatani
,
Shotaro Takano
· 1 min read
Abstract
Applying Gaussian process regression (GPR), a nonparametric probabilistic method, enables the correction for secondary ion mass spectrometry (SIMS) matrix effects while providing quantitative prediction uncertainty. Using magnesium isotope datasets of olivine reference materials, the GPR model achieved high prediction accuracy for instrumental mass fractionation and demonstrated its effectiveness for correcting matrix effects influenced by minor elements in solid-solution minerals.
Type
Publication
Rapid Communications in Mass Spectrometry, 39(13), e10038 (2025)
Status
Peer-reviewed Open access
publications

Key Highlights

  • Uncertainty Quantification: Demonstrates the application of Gaussian process regression (GPR) to correct SIMS matrix effects while providing quantitative prediction uncertainties.
  • Minor Element Impact: Reveals that minor elements in olivine (Ca, Cr, Mn) independently impact instrumental mass fractionation (IMF) during SIMS Mg isotope analysis.
Machine Learning SIMS Analytical Chemistry
Keita Itano
Authors
Keita Itano (he/him)
Research Scientist
I am a Research Scientist at JAMSTEC, specializing in geochemistry and the application of machine learning to Earth sciences. My research focuses on mineral geochemistry, isotope analysis, and data-driven approaches to understanding geological processes. I am passionate about integrating advanced analytical techniques with machine learning to explore Earth’s history and mineral resources.