MIMA—a software for analyte identification in MCC/IMS chromatograms by mapping accompanying GC/MS measurements
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Ion mobility spectrometry coupled to multi capillary columns (MCC/IMS) combines highly sensitive spectrometry with a rapid separation technique. MCC\IMS is widely used for biomedical breath analysis. The identification of molecules in such a complex sample necessitates a reference database. The existing IMS reference databases are still in their infancy and do not allow to actually identify all analytes. With a gas chromatograph coupled to a mass selective detector (GC/MSD) setup in parallel to a MCC/IMS instrumentation we may increase the accuracy of automatic analyte identification. To overcome the time-consuming manual evaluation and comparison of the results of both devices, we developed a software tool MIMA (MS-IMS-Mapper), which can computationally generate analyte layers for MCC/IMS spectra by using the corresponding GC/MSD data. We demonstrate the power of our method by successfully identifying the analytes of a seven-component mixture. In conclusion, the main contribution of MIMA is a fast and easy computational method for assigning analyte names to yet un-assigned signals in MCC/IMS data. We believe that this will greatly impact modern MCC/IMS-based biomarker research by “giving a name” to previously detected disease-specific molecules.
KeywordsVolatile organic compounds (VOCs) Layer Automation Multi-capillary column ion mobility spectrometry (MCC/IMS) Gas chromatography/mass selective detector (GC/MSD) Analyte identification
The financial support of the Ministry of Education Science and Technology (MEST) of the Republic of Korea is acknowledged thankfully (KE). Part of the work of this paper has been supported by the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center (Sonderforschungsbereich) SFB 876 Providing Information by Resource-Constrained Analysis, project TB1 Resource-Constrained Analysis of Spectrometry Data (JIBB). JB is grateful for financial support from the Cluster of Excellence for Multimodel Computing and Interaction and the Villum Foundation. ACH is grateful for financial aid provided by the International Max Planck Research School, Saarbrücken, Germany. We thank Joachim Müller for software development.
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