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.

中文翻译：

---

关于将电泳图转换为有效电泳迁移率范围的新见解


CE-MS 作为代谢组学分析工具越来越受到青睐，因为它能够获取生物样品中极性最强的元素的信息。这得益于通过电泳图（流动图）的迁移率规模表示来提高峰识别的鲁棒性。作为促进使用 CE-MS 处理非靶向代谢组学数据的必要步骤，作者之前开发并推出了 ROMANCE，这是一种通过简单且独立的用户界面自动生成大型非靶向数据集的流动图的软件。在此，我们介绍了 ROMANCE 的新版本，包括新功能，例如与其他类型数据（目标 MS 数据和 2D UV-Vis 吸收型电泳图）的兼容性，以及转换参数中急需的额外灵活性（包括场斜坡和二级标记的使用），更多的测量条件（取决于检测和积分模式），最重要的是解决定量峰转换的问题。首先，我们回顾了当前有关峰表征的理论框架，并开发了用于多标记峰面积校正、预测峰位置精度和评估峰形状失真的新公式。然后，提出了新版本的软件并进行了实验验证。我们将多标记迁移率转换与之前的结果进行对比，发现峰位置精度有所提高，最后我们展示了在实际非靶向代谢组学数据中的应用。