Machine-learning-enhanced time-of-flight mass spectrometry analysis
File(s)
Author(s)
Type
Journal Article
Abstract
Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.
Date Issued
2021-02-12
Date Acceptance
2020-12-17
Citation
Patterns, 2021, 2 (2), pp.100192-100192
ISSN
2666-3899
Publisher
Elsevier
Start Page
100192
End Page
100192
Journal / Book Title
Patterns
Volume
2
Issue
2
Copyright Statement
Copyright 2020 The Author(s).
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33659909
PII: S2666-3899(20)30262-2
Subjects
atom probe tomography
machine learning
pattern recognition
secondary ion mass spectrometry
time-of-flight mass spectrometry
Publication Status
Published online
Coverage Spatial
United States