Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
J Am Soc Mass Spectrom. 2021 Jun 2;32(6):1412-1423. doi: 10.1021/jasms.1c00032. Epub 2021 May 24.
Use of high-resolution mass spectrometry (HRMS) including a MS calibration method has enabled simultaneous identification and quantification of knowns/unknowns. This has expanded our knowledge about the existing sample relevant chemical space in a way beyond reconciliation with a quantification task. This is largely due to fact that reference standards are not always available to achieve quantitative analysis. In this scenario, a semi-quantitative approach can fill the gap and provide a rough estimation of concentration. This research aimed to develop and compare several semi-quantification approaches based on chemical similarity or properties. The ionization efficiency scale was created for several groups of natural products. Advanced modeling approach based on a support vector machine was conducted to learn from the experimental ionization efficiency and apply it to unknowns or suspected compounds to predict their ionization efficiency in electrospray ionization mode. The developed semi-quantification workflows could be useful in most HRMS based "omics" areas, especially in natural products discovery.
使用高分辨率质谱（HRMS），包括一种 MS 校准方法，能够同时鉴定和定量已知物/未知物。这以一种超越与定量任务协调的方式扩展了我们对现有样本相关化学空间的认识。这在很大程度上是因为并非总是可以获得参考标准来进行定量分析。在这种情况下，半定量方法可以填补空白并提供浓度的大致估计。本研究旨在开发和比较几种基于化学相似性或特性的半定量方法。为几类天然产物创建了电离效率标度。基于支持向量机的先进建模方法用于从实验电离效率中学习，并将其应用于未知物或可疑化合物，以预测它们在电喷雾电离模式下的电离效率。开发的半定量工作流程可用于大多数基于 HRMS 的“组学”领域，特别是在天然产物发现中。
