氯化石蜡
化学
质谱法
色谱法
轨道轨道
电喷雾电离
分辨率(逻辑)
气相色谱法
电喷雾质谱
电喷雾
高分辨率
有机化学
计算机科学
遥感
地质学
人工智能
作者
Marie Mézière,Ronan Cariou,Frédéric Larvor,Emmanuelle Bichon,Yann Guitton,Philippe Marchand,Gaud Dervilly,Bruno Le Bizec
标识
DOI:10.1016/j.chroma.2020.460927
摘要
Chlorinated paraffins (CPs), or polychlorinated n-alkanes, form a complex family of chemicals as they exist as mixtures of several thousands of isomers. To facilitate their classification, they are subdivided into short-chains (C10C13, SCCPs), medium-chains (C14C17, MCCPs), and long-chains (C≥18, LCCPs) and further subdivided according to their chlorination degree. Until recently, the most common strategy implemented for their analysis was GC-ECNI-LRMS, with the main disadvantage being the high dependence of the response to the chlorination degree and the incapability of analysing LCCPs. In this work, we developed a method based on liquid chromatography coupled with electrospray ionisation-Orbitrap mass spectrometry (LC-ESI-HRMS) to expand the analysis capabilities of CPs. Although the different physico-chemical properties of CPs have led to compromises on the choice of analytical parameters, the addition of a mixture of DCM/ACN post-column with appropriate LC-ESI(-)-HRMS parameters enabled optimal and simultaneous detection of SCCPs, MCCPs and LCCPs from 10 to 36 carbons in one single injection. The combination of both the optimised LC-ESI parameters and the high resolution of the mass spectrometer (R = 140,000 @200 m/z) allowed separation of CPs signals of interest from unwanted halogenated ones, leading to minimum interferences in the detection. The optimised method was then successfully applied to the characterization of three types of vegetable oil, which were mostly contaminated with MCCPs. Additionally, the implementation of the LCHRMS strategy enabled the identification of highly chlorinated LCCPs in edible oil for the first time at dozens of ng g-1 lw, which demonstrates the need of such comprehensive methods to expand the knowledge about CPs occurrence in food and environmental matrices.
科研通智能强力驱动
Strongly Powered by AbleSci AI