Trends and Innovations in Tools for Processing Chromatographic Data Using Mass Spectrometry Detection: A Systematic...

Area of Science:

• Analytical Chemistry
• Computational Science
• Data Science

Background:

• Chromatographic data processing faces challenges due to complex samples and large data volumes from techniques like mass spectrometry (MS).
• Technological innovations are crucial for efficient and accurate analysis of this complex data.

Purpose of the Study:

• To systematically review technological innovations in computational tools for chromatographic data processing over the last six years.
• To identify advancements in algorithms and software for handling complex analytical data.

Main Methods:

• Systematic literature review following the PRISMA protocol.
• Searches conducted across five major scientific databases (SciFinder, Scopus, Web of Science, Embase, ScienceDirect).
• Selection of 33 studies based on originality, applicability, and innovation in analytical tools.

Main Results:

• Significant progress in algorithms for peak detection, alignment, and deconvolution, particularly using machine learning, deep learning, and multivariate resolution.
• Emergence of automated and scalable tools (e.g., DeepResolution, SeA-M2Net, SLAW) improving accuracy in noise filtering, baseline correction, and compound identification.
• Increased development and adoption of open-source software, enhancing accessibility and interoperability.

Conclusions:

• Recent advancements offer more robust, accessible, and adaptable solutions for chromatographic data processing.
• Continued challenges include the need for annotated data and standardization.
• These innovations expand analytical capabilities across scientific and industrial fields.