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As a key advance, most core <jats:italic>MSS<\/jats:italic> functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and\/or efficiency. <jats:italic>MSS<\/jats:italic> reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and\u2009~\u200952% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate <jats:italic>MSS<\/jats:italic> functionalities and demonstrate reliability. <jats:italic>MSS<\/jats:italic> expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.<\/jats:p>\n                <jats:p><jats:bold>Graphical abstract<\/jats:bold><\/jats:p>","DOI":"10.1186\/s13321-023-00741-9","type":"journal-article","created":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T06:01:44Z","timestamp":1695448904000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis"],"prefix":"10.1186","volume":"15","author":[{"given":"Ximin","family":"Hu","sequence":"first","affiliation":[]},{"given":"Derek","family":"Mar","sequence":"additional","affiliation":[]},{"given":"Nozomi","family":"Suzuki","sequence":"additional","affiliation":[]},{"given":"Bowei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Katherine T.","family":"Peter","sequence":"additional","affiliation":[]},{"given":"David A. 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