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

• URL: http://arxiv.org/abs/2010.01030v1
• Date: Fri, 2 Oct 2020 14:35:47 GMT
• Title: Machine-learning-enhanced time-of-flight mass spectrometry analysis
• Authors: Ye Wei, Rama Srinivas Varanasi, Torsten Schwarz, Leonie Gomell, Huan Zhao, David J. Larson, Binhan Sun, Geng Liu, Hao Chen, Dierk Raabe, and Baptiste Gault
• Abstract summary: We introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds. 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.
• Score: 10.16825220733013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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