Analytical Techniques Applied in Metabolomics
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 145
Special Issue Editors
Interests: metabolomics; analytical chemistry; chemometrics; food chemistry; bio-active compounds; mycotoxins; nanomaterials
Special Issue Information
Dear Colleagues,
Metabolomics has become a key discipline for understanding biological systems, providing comprehensive information about the small molecules involved in metabolic pathways. The rapid expansion of this field has been driven largely by continuous developments in analytical technologies, which enable the detection, identification, and quantification of an increasingly wide range of metabolites in complex biological matrices. However, due to the chemical diversity of metabolites, no single analytical technique is capable of covering the entire metabolome. As a result, metabolomics relies heavily on advanced analytical strategies combining separation techniques with powerful detection systems.
In recent years, significant progress has been achieved in analytical methodologies applied to metabolomics. Techniques such as liquid chromatography–mass spectrometry (LC–MS), gas chromatography–mass spectrometry (GC–MS), ion mobility spectrometry, and nuclear magnetic resonance (NMR) spectroscopy have greatly improved in terms of sensitivity, resolution, and metabolome coverage. At the same time, advances in sample preparation, data acquisition strategies, and computational tools for data processing and metabolite annotation are continuously expanding the capabilities of metabolomics studies.
This Special Issue, entitled “Analytical Techniques Applied in Metabolomics”, aims to highlight recent methodological developments and innovative analytical approaches that contribute to improving metabolomics workflows. We invite original research articles and review papers addressing advances in analytical instrumentation, separation techniques, sample preparation strategies, data processing, metabolite identification, and quantitative or qualitative metabolomics approaches. Contributions focusing on both targeted and untargeted metabolomics, as well as applications across biomedical, environmental, food and plant sciences, are also welcome.
Dr. María García Nicolás
Dr. Anil Meher
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- metabolomics
- analytical techniques
- mass spectrometry
- chromatography
- ion mobility spectrometry
- nuclear magnetic resonance (NMR)
- targeted and untargeted metabolomics
- sample preparation
- metabolite identification
- data processing and analysis
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