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New insights into the conversion of electropherograms to the effective electrophoretic mobility scale
Author(s) -
Codesido Santiago,
Drouin Nicolas,
Ferré Sabrina,
Schappler Julie,
Rudaz Serge,
GonzálezRuiz Víctor
Publication year - 2021
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.202000333
Subject(s) - electropherogram , computer science , software , robustness (evolution) , flexibility (engineering) , transformation (genetics) , biological system , data mining , metabolomics , computational biology , chemistry , electrophoresis , chromatography , mathematics , biology , statistics , biochemistry , gene , programming language
CE–MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility‐scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE–MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self‐contained user interface. Herein, we introduce a new version of ROMANCE including new features such as compatibility with other types of data (targeted MS data and 2D UV‐Vis absorption‐like electropherograms), and the much needed additional flexibility in the transformation parameters (including field ramping and the use of secondary markers), more measurement conditions (depending on detection and integration modes), and most importantly tackling the issue of quantitative peak conversion. First, we present a review of the current theoretical framework with regard to peak characterization, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. Then, the new version of the software is presented and validated experimentally. We contrast the multiple marker mobility transformations with previous results, finding increased peak position precision, and finally we showcase an application to actual untargeted metabolomics data.