作者
Christian P Haas, Maximilian Lubbesmeyer, Edward H Jin, Matthew A McDonald, Brent A Koscher, Nicolas Guimond, Laura Di Rocco, Henning Kayser, Samuel Leweke, Sebastian Niedenfuhr, Rachel Nicholls, Emily Greeves, David M Barber, Julius Hillenbrand, Giulio Volpin, Klavs F Jensen
发表日期
2023/2/9
期刊
ACS Central Science
卷号
9
期号
2
页码范围
307-317
出版商
American Chemical Society
简介
Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors’ hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC–DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA’s data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction …
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