Automated Peak Annotation in Time-of-Flight Secondary Ion Mass Spectrometry via a Physics-Informed Probabilistic...

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Peak annotation in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a manual and time-consuming process.
• Assigning chemical formulas to fragment ion peaks is a critical bottleneck in ToF-SIMS data analysis.

Purpose of the Study:

• To develop and evaluate a physics-informed probabilistic framework for automating ToF-SIMS peak annotation.
• To improve the accuracy and efficiency of chemical formula assignment in ToF-SIMS spectra.

Main Methods:

• A probabilistic framework combining five chemically motivated constraints: mass accuracy, element composition priors, isotope patterns, nitrogen rule, and valence bounds.
• Evaluation on 643 ground-truth peaks from 151 compounds in positive and negative ion modes.
• Assessment in both scoring and fully automated end-to-end deployment scenarios.

Main Results:

• The framework achieved 52.3% Top-1 and 76.4% Top-3 accuracy in scoring tasks, a 4.9-fold improvement over mass-only scoring.
• In end-to-end deployment, Top-1 accuracy was 26.3%, limited by candidate generation.
• Element composition priors were the most influential constraint, followed by isotope matching and the nitrogen rule.

Conclusions:

• The developed framework offers a practical computational foundation for automated ToF-SIMS spectrum interpretation.
• It relies on physically motivated priors and curated databases, requiring no labeled training spectra.
• The interpretable per-constraint scores and polarity-specific configurations enhance its utility.