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

ABSTRACT:

Rationale

Matrix effects by secondary ion mass spectrometry (SIMS) are empirically corrected by calibration using matrix-matched reference materials. However, conventional parametric regression cannot estimate the prediction uncertainty to account for the difference in compositions of new data and reference materials. Applying Gaussian process regression (GPR), a nonparametric probabilistic method, enables the correction for matrix effect while providing quantitative prediction uncertainty.

Methods

We developed GPR models for estimating instrumental mass fractionation (IMF). Magnesium isotope dataset of 17 olivine reference materials was used as training data, and the developed model was applied to another data set of extraterrestrial olivines.

Results

The GPR model u

SUBMITTER: Itano K 

PROVIDER: S-EPMC11959212 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature