AI project targets historical mass spectrometry data

Ahead of the 74th ASMS Conference, mzio GmbH has announced two new funding awards supporting its next phase of innovation in mass spectrometry data analysis and calibration technologies.

At ASMS 2026, the company will unveil FAIR-MS, an AI-powered initiative designed to unlock the value of historical mass spectrometry data by enabling automated comparison and reuse across large archived datasets. The €150,000 project is funded through the Bremer Aufbau-Bank (BAB) under the FEI programme, co-financed by the European Union via EFRE-Bremen 2021–2027. FAIR-MS will allow researchers to compare LC-MS and GC-MS data regardless of instrument type, laboratory, or experimental conditions, reducing manual effort while improving compound identification and chemical annotation. Integrated directly into existing workflows, the system aims to make long-term data reuse a practical reality.

In parallel, mzio is rolling out major enhancements to its mzmine platform, its vendor-agnostic mass spectrometry software. Updates include expanded instrument format support, new lipid and small-molecule dashboards, and an impurity analysis workflow that links orthogonal detector outputs with MS data. The platform continues to be developed in collaboration with leading instrument vendors including SCIEX, Waters, Agilent, and Bruker.

"With FAIR-MS we are turning the FAIR data principles into practical reality," said Dr Ansgar Korf, CEO of mzio GmbH. "The ability to automatically search and match new measurements against large collections of historical MS data will fundamentally change how researchers and industrial users extract value from their data."

mzio has also been awarded €217,758 under Germany’s ZIM programme for the LIMMIC project, a German–Swedish collaboration with Polymer Factory Sweden AB and CeMOS at the Technical University of Applied Sciences Mannheim. LIMMIC focuses on standardised calibration across LC-MS, ion mobility spectrometry, and MS imaging, improving reproducibility in multidimensional workflows, particularly for small metabolites and lipid oxidation studies.

At ASMS 2026, mzio will present three posters covering LC-MS data unification, deep learning molecular networking for LC-TIMS-MS dereplication, and comparisons of DDA and DIA lipidomics.

The company will be exhibiting at booth #608 to demonstrate new mzmine features and discuss collaborations around FAIR-MS, LIMMIC, and vendor-integrated workflows.

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