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Visualization of Ambient Mass Spectrometry with the Use of Schlieren Photography
Published on: June 20, 2016
Modern machine-learning applications in ambient ionization mass spectrometry
Anatoly A Sorokin1, Stanislav I Pekov2,3,4, Denis S Zavorotnyuk1
1Laboratory of Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Abstract:
This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.
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