优先次序
质谱法
化学
环境化学
鉴定(生物学)
色谱法
高分辨率
质量
生物累积
环境科学
生化工程
质谱
生物
生态学
地质学
遥感
管理科学
经济
工程类
作者
Boris Bugsel,Jonathan Zweigle,Christian Zwiener
标识
DOI:10.1016/j.teac.2023.e00216
摘要
Per- and polyfluoroalkyl substances (PFAS) are a large group of more than 4700 individual compounds which are applied in a wide range of applications in industrial processes and consumer products due to their water and oil repellency and surfactant properties. Concerns on PFAS arise from the very high stability, bioaccumulation potential and toxicity and the ubiquitous occurrence in humans, animals, soils, sediments, surface, ground and drinking waters. Advanced analytical methods are needed to investigate the input and fate of PFAS and potential transformation products in the environment and the exposure pathways for humans and wildlife. Therefore, nontarget screening (NTS) methods by high-resolution mass spectrometry (HRMS) coupled to chromatography are often applied to meet the analytical challenges arising from the high number and chemical diversity of individual compounds, the lack of authentic standards and information on identity and application areas. In this critical review we discuss the recent advances of NTS workflows applied to detect and identify PFAS based on the intrinsic information contained in data from chromatography and HRMS data on the MS1 and MS2 level. This includes retention time and peak shape characteristics, data on accurate mass and isotopologues, and high-resolution mass fragments. Successful approaches for prioritization and identification of PFAS are mostly based on mass defect filtering, Kendrick mass defect analysis, mass matches with suspect lists, assignment of chemical formulas, mass fragmentation patterns, diagnostic fragments and fragment mass differences. So far NTS approaches for PFAS were able to identify more than 750 compounds. However, still limited applicability of chromatography and ionization methods and limited mass resolving power and accuracy largely restrict a complete identification of a high number of unknown PFAS in complex samples from environmental compartments and biota.
科研通智能强力驱动
Strongly Powered by AbleSci AI