State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China; Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 999077, China.
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077, China.
Chemosphere. 2020 Dec;260:127458. doi: 10.1016/j.chemosphere.2020.127458. Epub 2020 Jun 28.
Advances in the ultra-high-resolution mass spectroscopy lead to a deep insight into the molecular characterization of natural organic matter (NOM). Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) has been used as one of the most powerful tools to decipher NOM molecules. In FTICR-MS analysis, the matrix effects caused by the co-occurring inorganic substances in water samples greatly affect the ionization of NOM molecules. The inherent complexity of NOM may hinder its component classification and formula assignment. In this study, basic principles and recent advances for sample separation and purification approaches, ionization methods, and the evolutions in formula assignment and data exploitation of the FTICR-MS analysis were reviewed. The complementary characterization methods for FTICR-MS were also reviewed. By coupling with other developed/developing characterization methods, the statistical confidence for inferring the NOM compositions by FTICR-MS was greatly improved. Despite that the refined separation procedures and advanced data processing methods for NOM molecules have been exploited, the big challenge for interpreting NOM molecules is to give the basic structures of them. Online share of the FTICR-MS data, further optimizing the FTICR-MS technique, and coupling this technique with more characterization methods would be beneficial to improving the understanding of the composition and property of NOM.
超高分辩质谱技术的进步使人们深入了解天然有机物(NOM)的分子特征。傅里叶变换离子回旋共振质谱(FTICR-MS)已被用作解析 NOM 分子的最有力工具之一。在 FTICR-MS 分析中,水样中共同存在的无机物引起的基质效应极大地影响了 NOM 分子的电离。NOM 的固有复杂性可能阻碍其成分分类和公式赋值。本研究综述了样品分离和纯化方法、离子化方法、公式赋值和 FTICR-MS 分析数据开发的最新进展的基本原理。还综述了 FTICR-MS 的补充表征方法。通过与其他已开发/正在开发的表征方法相结合,大大提高了通过 FTICR-MS 推断 NOM 成分的统计置信度。尽管已经开发了用于 NOM 分子的精细分离程序和先进的数据处理方法,但解释 NOM 分子的最大挑战是给出它们的基本结构。FTICR-MS 数据的在线共享、进一步优化 FTICR-MS 技术以及将该技术与更多表征方法相结合,将有助于提高对 NOM 组成和性质的理解。