Machine-learning-enhanced time-of-flight mass spectrometry analysis

Bibliographic Details
Title: Machine-learning-enhanced time-of-flight mass spectrometry analysis
Authors: Wei, Ye, Varanasi, Rama Srinivas, Schwarz, Torsten, Gomell, Leonie, Zhao, Huan, Larson, David J., Sun, Binhan, Liu, Geng, Chen, Hao, Raabe, Dierk, Gault, Baptiste
Publication Year: 2020
Collection: Computer Science
Condensed Matter
Subject Terms: Condensed Matter - Materials Science, Computer Science - Machine Learning
More Details: Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual user's 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.
Comment: 20 pages, 15 figures
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2010.01030
Accession Number: edsarx.2010.01030
Database: arXiv
More Details
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