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arXiv · 2010.01030

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

Ye Wei·Rama Srinivas Varanasi·Torsten Schwarz·Leonie Gomell·Huan Zhao·David J. Larson·Binhan Sun·Geng Liu·Hao Chen·Dierk Raabe·Baptiste Gault
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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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Ye Wei, Rama Srinivas Varanasi, Torsten Schwarz, Leonie Gomell, Huan Zhao, David J. Larson, Binhan Sun, Geng Liu, Hao Chen, Dierk Raabe, Baptiste Gault. 2020-10-02. Machine-learning-enhanced time-of-flight mass spectrometry analysis. https://arxiv.org/abs/2010.01030

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