Bionorica research GmbH (subsidiary of Bionorica SE), Mitterweg 24, 6020 Innsbruck, Austria.
Department of Neuroradiology, Medical University of Innsbruck, Anichstraße 35, 6020 Innsbruck, Austria.
Anal Chem. 2020 Oct 6;92(19):12909-12916. doi: 10.1021/acs.analchem.0c01447. Epub 2020 Sep 25.
A holistic, nontargeted mass spectrometric analysis of any herbal material and preparation is intimately connected to fast chemical profiling and visualization of secondary plant metabolite classes or single compounds. High-resolution mass spectral data enable a broad variety of analytical possibilities. Often a fast and comprehensive overview on compound classes (phytochemical profiling) is needed before single-substance considerations. We present a fast approach for the initial characterization and substance class profiling using relative mass defect plots for the visualization of herbal compositions. From a dataset of 1160 common plant metabolites that represent a varied mixture of molecular classes in polarity, glycosylation, and alkylation, manually annotated for substance classes, the relative mass defects were calculated using theoretical molecular masses. For the calculation of the relative mass defect, a new approach incorporating two correction functions to obtain correct relative mass defect results also for large hydrocarbons, and a multitude of polyhalogenated molecules was developed. Using the Khachyan algorithm, elliptical areas clustering substance classes within the relative mass defect plots were calculated. The resulting novel relative mass defect plots provide a quick way of two-dimensional substance class mapping directly from high-resolution mass spectral data and may be considered as a unique fingerprint for herbals, part of them or herbal preparations. We show that adding the retention time as a third dimension improves the resolution power of the two-dimensional relative mass defect plot and offers the possibility for a more detailed substance class mapping.
对任何草药材料和制剂进行整体、非靶向的质谱分析,都与快速化学分析和可视化次生植物代谢物类别或单一化合物密切相关。高分辨率质谱数据可实现多种分析可能性。通常在考虑单一物质之前,需要快速全面地了解化合物类别(植物化学分析)。我们提出了一种快速方法,用于使用相对质量缺陷图进行初始特征描述和物质类别分析,以可视化草药成分。从一个由 1160 种常见植物代谢物组成的数据集开始,这些代谢物代表了极性、糖基化和烷基化等不同分子类别中多样化的混合物,这些代谢物已经被手动注释为物质类别,并使用理论分子量计算相对质量缺陷。为了计算相对质量缺陷,我们开发了一种新方法,该方法采用了两个校正函数,可以为大的碳氢化合物和大量多卤代分子获得正确的相对质量缺陷结果。使用 Khachyan 算法,在相对质量缺陷图中计算出椭圆形区域聚类物质类别。由此产生的新颖相对质量缺陷图提供了一种从高分辨率质谱数据直接进行二维物质类别映射的快速方法,可被视为草药、部分草药或草药制剂的独特指纹。我们表明,添加保留时间作为第三维可以提高二维相对质量缺陷图的分辨率能力,并提供更详细的物质类别映射的可能性。