Improvement and Development of Robust Quality Assurance Procedures for Untargeted Metabolomics Workflow
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 3407
Special Issue Editors
Interests: LC–MS; GC–MS; CE–MS; untargeted metabolomics; clinical application of metabolomics
Interests: untargeted metabolomics; lipidomics; mass spectrometry; LC-MS; GC-MS; CE-MS; clinical metabolomics; food metabolomics
2. NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, 6229ER Maastricht, The Netherlands
Interests: volatile metabolite; exhaled breath; fecal headspace analysis; machine learning; gut microbiome
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Special Issue Information
Dear Colleagues,
Untargeted metabolomics powered by state-of-the-art advanced analytical solutions allows the transformation of complex data into valuable discoveries. However, advancing in metabolomics requires consensus for quality assurance and quality control (QA/QC) procedures. Standardization of the minimum requirements and best practices are crucial and should cover the whole workflow through experimental design, sampling, monitoring the precision of the analytical process, data processing, and treatment, as well as metabolite annotation. QA/QC provide a higher level of data confidence and reliability, especially for monitoring quality over time and comparing data across workflows or laboratories. Protocols, guidelines, and manuscripts that address data quality management in the untargeted metabolomics studies are welcome in this Special Issue. Special interest will be given for the studies reporting from considerably challenging matrices, such as extracellular vesicles, cells, and tissues, or analysis of volatile organic compounds.
Dr. Danuta Dudzik
Dr. Mónica P. Cala
Dr. Agnieszka M. Smolinska
Dr. Antonia García
Guest Editors
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Keywords
- quality assurance
- quality control
- untargeted metabolomics
- quality assessment
- data reliability
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