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

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Wei,  Ye
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Varanasi,  Rama Srinivas
Alloy Design and Thermomechanical Processing, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Schwarz,  Torsten
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Gomell,  Leonie
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Zhao,  Huan
Alloy Design and Thermomechanical Processing, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Sun,  Binhan
Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Raabe,  Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Gault,  Baptiste
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK;

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Citation

Wei, Y., Varanasi, R. S., Schwarz, T., Gomell, L., Zhao, H., Larson, D. J., et al. (2021). Machine-learning-enhanced time-of-flight mass spectrometry analysis. Patterns, 2(2): 100192. doi:10.1016/j.patter.2020.100192.


Cite as: http://hdl.handle.net/21.11116/0000-0008-0E8D-B
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. Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data. A machine-learning application for the accelerated data processing and interpretation of time-of-flight mass spectrometry is presented. The machine learns patterns in a human-label-free manner, making the process easy to implement and the result highly reproducible. © 2020 The Authors