Biological Systems Engineering, Washington State University, Pullman, Washington, 99163, USA.
LCP-A2MC, FR 3624, Université de Lorraine, ICPM, 57078, Metz Cedex 03, France.
ChemSusChem. 2020 Sep 7;13(17):4428-4445. doi: 10.1002/cssc.202000239. Epub 2020 Apr 2.
The lack of standards to identify oligomeric molecules is a challenge for the analysis of complex organic mixtures. High-resolution mass spectrometry-specifically, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS)-offers new opportunities for analysis of oligomers with the assignment of formulae (C H O ) to detected peaks. However, matching a specific structure to a given formula remains a challenge due to the inability of FT-ICR MS to distinguish between isomers. Additional separation techniques and other analyses (e.g., NMR spectroscopy) coupled with comparison of results to those from pure compounds is one route for assignment of MS peaks. Unfortunately, this strategy may be impractical for complete analysis of complex, heterogeneous samples. In this study we use computational stochastic generation of lignin oligomers to generate a molecular library for supporting the assignment of potential candidate structures to compounds detected during FT-ICR MS analysis. This approach may also be feasible for other macromolecules beyond lignin.
缺乏识别低聚分子的标准是分析复杂有机混合物的一个挑战。高分辨率质谱,特别是傅里叶变换离子回旋共振质谱(FT-ICR MS),为分析低聚分子提供了新的机会,可以通过分配式(C H O )来确定检测到的峰。然而,由于 FT-ICR MS 无法区分异构体,因此将特定结构与给定式相匹配仍然是一个挑战。此外,还可以采用其他分离技术和其他分析方法(例如核磁共振波谱),并将结果与纯化合物的结果进行比较,从而确定 MS 峰的归属。不幸的是,对于复杂的、不均匀的样品的完全分析,这种策略可能不切实际。在本研究中,我们使用计算随机生成木质素低聚物来生成一个分子库,以支持将在 FT-ICR MS 分析过程中检测到的化合物的潜在候选结构分配给它们。这种方法对于木质素以外的其他大分子也可能是可行的。