2020/10/02 by Ye Wei, Rama Srinivas Varanasi, Wei, Ye +18 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #Ion-surface interactions and analysis #Machine Learning (cs.LG) #Mass Spectrometry Techniques and Applications #Materials Science (cond-mat.mtrl-sci) #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.48550/arxiv.2010.01030
openalex publication_date 2020/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.