Integration of Mass Spectrometry with Chemometrics and AI: A New Era in Analytical Chemistry

Mass spectrometry (MS) has become one of the most powerful analytical techniques in chemistry, biology, environmental sciences, and medicine, owing to its high sensitivity, selectivity, and ability to provide detailed molecular information. However, as modern research problems grow in complexity, single MS techniques often face limitations in resolving overlapping signals, identifying unknown compounds, or processing large and multidimensional datasets. To address these challenges, researchers have increasingly relied on chemometrics and artificial intelligence (AI). These approaches allow deeper data mining, pattern recognition, and predictive modeling, greatly expanding the analytical power of MS. The integration of MS with advanced data-driven strategies and complementary analytical methods represents an emerging frontier, opening new opportunities for accurate, efficient, and robust analysis across diverse applications.

There are several issues and challenges associated with analyses using conventional mass spectrometric techniques. Furthermore, relying solely on a single mass spectrometry approach is often insufficient for addressing the diverse and complex analytical and detection problems encountered in practice. In addition, advances in AI technologies have enabled researchers to directly predict information such as MS/MS spectra, retention time, and drift time from molecular structures, providing more efficient workflows and valuable references for the development of mass spectrometry methods. Therefore, the aim of this Research Topic is to highlight promising, recent, and novel trends in the development of MS techniques and the integration of MS with other approaches, including (but not limited to) complementary analytical methods, data processing strategies, and AI.

We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:
• Combination of MS with other analytical methods to address analytical challenges that conventional MS methods can hardly overcome.
• Application of advanced data processing approaches with MS, enabling faster, more accurate, and more reliable results.
• Integration of MS with AI techniques (including, but not limited to, deep learning, machine learning, and large models) to enhance efficiency in diverse fields such as tumor marker identification, drug screening, and the assessment of exposure risks to environmental pollutants.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Mass spectrometry, Chemometrics, Artificial intelligence, Data integration, Novel techniques

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.