DBDIpy: a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry.

Area of Science:

• Analytical Chemistry
• Computational Chemistry

Background:

• Plasma ionization is increasingly used for mass spectrometry (MS) studies of volatiles and aerosols.
• Interpreting plasma ionization MS data is challenging due to competing ionization pathways and numerous ion species.
• Current tools lack the ability to detect adducts and in-source fragments, leading to ambiguous data evaluation.

Purpose of the Study:

• To develop a computational tool for processing and analyzing untargeted, time-sensitive plasma ionization MS datasets.
• To enable the identification of in-source fragments and rivaling ionization pathways.
• To facilitate the analysis of untargeted metabolomics data within the Python ecosystem.

Main Methods:

• Development of DBDIpy, a Python library (Version ≥ 3.7).
• Implementation of core functionalities for identifying in-source fragments and ionization pathways in time-sensitive datasets.
• Integration of elementary functions for untargeted metabolomics data processing and interfaces to existing MS data analysis tools.

Main Results:

• DBDIpy provides a solution for the ambiguous interpretation of plasma ionization MS data.
• The library effectively identifies in-source fragments and distinguishes between competing ionization pathways.
• Facilitates the analysis of complex, untargeted MS datasets, particularly those involving volatiles and aerosols.

Conclusions:

• DBDIpy enhances the analysis of plasma ionization MS data by addressing the challenge of complex ion species.
• The tool improves data interpretation accuracy and reduces ambiguity in the study of volatiles and aerosols.
• DBDIpy is available via PyPI and GitHub, supporting the broader scientific community in MS data analysis.