A browser application for comprehensive 3-dimensional LC × LC × IM – MS data analysis to study grape and wine polyphenols
The analysis of structurally diverse proanthocyanidins in grapes and wine is challenging. Comprehensive two-dimensional liquid chromatography (LC×LC) and ion mobility spectrometry-mass spectrometry (IMS-MS) are increasingly used to address the challenges associated with the analysis of highly complex samples such as wine and grapes. Hyphenation of these techniques in form of a comprehensive three-dimensional LC×LC×IMS separation system coupled to high resolution – mass spectrometry significantly increases separation power. The gain of separation power results in very opulent and complex four-dimensional data structure. One of the main challenges of such a system is the lack of commercial software to accommodate the resulting four-dimensional data. We therefore developed a Python protocol using Jupyter notebooks for the extraction, visualization and interpretation of such data. Jupyter notebooks allow all methods of signal and data processing and even interactive visualizations. The user, however, needs programming skills to employ the notebooks. To make the data analysis approach available to analytical chemists without programming skills, we developed an interactive analytical browser application based on the Python package Plotly’s Dash.
The visualization of an extracted ion chromatogram (EIC) of the LC×LC×IMS-MS data is achieved by a 3-dimensional scatter plot representing the first and second dimension retention times and the IMS drift time on the x-, y- and z-axis of the scatter plot, respectively. Peaks appear as clouds of data points in this three-dimensional space. A mouse click on a data point shows the high-resolution mass spectrum in a separate bar plot. An example of the usage of the browser app includes separations of the procyanidin trimers (865 m/z) found in grape seed extract
Issue: IVAS 2022
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comprehensive multidimensional chromatography, liquid chromatography, ion mobility spectrometry, data analysis, polyphenols