Machine-learning-enhanced time-of-flight mass spectrometry analysis.
atom probe tomography
machine learning
pattern recognition
secondary ion mass spectrometry
time-of-flight mass spectrometry
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
12 Feb 2021
12 Feb 2021
Historique:
received:
29
09
2020
revised:
13
11
2020
accepted:
17
12
2020
entrez:
4
3
2021
pubmed:
5
3
2021
medline:
5
3
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33659909
doi: 10.1016/j.patter.2020.100192
pii: S2666-3899(20)30262-2
pmc: PMC7892357
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100192Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare that there is no conflict of interest.
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