Modern machine-learning applications in ambient ionization mass spectrometry.
Anatoly A SorokinStanislav I PekovDenis S ZavorotnyukMariya M ShamraevaDenis S BormotovIgor A PopovPublished in: Mass spectrometry reviews (2024)
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.
Keyphrases
- artificial intelligence
- machine learning
- mass spectrometry
- big data
- gas chromatography
- liquid chromatography
- data analysis
- deep learning
- air pollution
- particulate matter
- capillary electrophoresis
- tandem mass spectrometry
- high resolution
- high performance liquid chromatography
- healthcare
- mental health
- electronic health record
- risk assessment
- climate change
- loop mediated isothermal amplification
- sensitive detection