色谱自动处理:用于化学反应分析中智能峰值识别和解卷积的带图形用户界面的 Python 综合软件包

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jan Obořil, Christian P. Haas, Maximilian Lübbesmeyer, Rachel Nicholls, Thorsten Gressling, Klavs F. Jensen, Giulio Volpin and Julius Hillenbrand
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引用次数: 0

摘要

反应筛选和高通量实验 (HTE) 与液相色谱法(高效液相色谱法和超高效液相色谱法)相结合,在合成化学中变得比以往任何时候都更加重要。随着实验数量的不断增加,确保正确的峰识别和整合变得越来越困难,特别是由于未知的副成分经常与感兴趣的峰重叠。我们开发了 MOCCA Python 软件包的改进版,该软件包具有基于网络的图形用户界面 (GUI),用于自动处理色谱图,包括基线校正、智能选峰、峰纯度检查、重叠峰解卷积和化合物跟踪。与 MOCCA 的前一版本相比,各个自动处理步骤都有所改进,使软件更加可靠和通用。使用三个数据集对算法的准确性进行了基准测试,并将其与之前的 MOCCA 实施方案和已公布的结果进行了比较。处理过程完全自动化,可加入校准和内标。该软件支持来自大多数商用高效液相色谱系统的带有光电二极管阵列检测器 (DAD) 数据的色谱图,Python 软件包和图形用户界面实现是开源的,允许添加新功能和进一步开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis

Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis

Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis

Reaction screening and high-throughput experimentation (HTE) coupled with liquid chromatography (HPLC and UHPLC) are becoming more important than ever in synthetic chemistry. With a growing number of experiments, it is increasingly difficult to ensure correct peak identification and integration, especially due to unknown side components which often overlap with the peaks of interest. We developed an improved version of the MOCCA Python package with a web-based graphical user interface (GUI) for automated processing of chromatograms, including baseline correction, intelligent peak picking, peak purity checks, deconvolution of overlapping peaks, and compound tracking. The individual automatic processing steps have been improved compared to the previous version of MOCCA to make the software more dependable and versatile. The algorithm accuracy was benchmarked using three datasets and compared to the previous MOCCA implementation and published results. The processing is fully automated with the possibility to include calibration and internal standards. The software supports chromatograms with photo-diode array detector (DAD) data from most commercial HPLC systems, and the Python package and GUI implementation are open-source to allow addition of new features and further development.

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CiteScore
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