Anatoly A. Sorokin, Stanislav I. Pekov, Denis S. Zavorotnyuk, Mariya M. Shamraeva, Denis S. Bormotov, Igor A. Popov
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引用次数: 0
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.
期刊介绍：
The aim of the journal Mass Spectrometry Reviews is to publish well-written reviews in selected topics in the various sub-fields of mass spectrometry as a means to summarize the research that has been performed in that area, to focus attention of other researchers, to critically review the published material, and to stimulate further research in that area.
The scope of the published reviews include, but are not limited to topics, such as theoretical treatments, instrumental design, ionization methods, analyzers, detectors, application to the qualitative and quantitative analysis of various compounds or elements, basic ion chemistry and structure studies, ion energetic studies, and studies on biomolecules, polymers, etc.
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