新型毛细管电泳—高分辨质谱联用技术在复杂样品分析中的应用

发布时间：2018-06-12 18:08

本文选题：毛细管电泳 + 毛细管电动分级提取　；参考：《福州大学》2014年博士论文

【摘要】：复杂样品的分离分析是近年分析科学领域的重要研究热点及难点之一。本论文在传统毛细管电泳的理论基础上建立一种全新的毛细管电泳技术——毛细管电动分级分离技术(CEkF),并将其与高分辨质谱技术联用,对几个典型的复杂样品进行分析鉴定。论文共分为七章,主要内容如下：第一章,介绍了复杂样品的定义及特征、毛细管电泳与高分辨质谱联用技术的特点及优势,并对本论文建立的毛细管电动分级分离技术的理论基础和方案设计进行详述。第二章,选取14个代表性模型化合物,采用CEkF技术,根据模型化合物的信号强度随基质pH变化的规律建立半经验模型,推导pKa的计算公式。该模型无需复杂的分离,实现了多种目标物pKa的同时测定以及结构的推测。第三章,在pKa测定模型的基础上,将建立的CEkF技术与傅里叶变换离子回旋共振(Fourier transform ion cyclotron resonance, FTICR)质谱准在线联用对葡萄酒中主要代谢物进行分析。实现了对葡萄酒重要标志性成分的鉴定,并证明了在极酸性条件下,CEkF技术对葡萄酒中的含硫化合物具有极高的选择性。第四章,利用CEkF技术对含硫化合物的高选择性,以及FTICR/MS的超高分辨率,实现对复杂样品二级有机气溶胶中水溶性有机化合物——有机磺酸酯的分析鉴定。在极酸性条件下,鉴定出生物燃烧质样品中有机磺酸酯以及硝基有机磺酸酯500余种。第五章,将制备型CEkF技术与超高分辨的FTICR/MS以及高场核磁共振(Nuclear magnetic resonance, NMR)技术准在线联用,实现了天然有机质代表物——Suwannee River黄腐酸(Suwannee River fulvic acid, SRFA)的分子层面的表征。这是电泳技术首次与NMR联用用于复杂样品的研究。第六章,采用CE-ESI/MS检测联用技术,以电动进样的场放大(Field amplified sample stacking, FASS)技术作为在线富集方法,实现了藻类样品中微囊藻毒素异构体的同时分离检测以及尿液中β2-激动剂的快速、灵敏的分离检测。第七章,采用高效液相色谱-三重四级杆串联质谱技术对失忆性贝毒——软骨藻酸的稳定性进行考察。并在最佳色谱条件下,成功实现了海水样品及贝类样品中软骨藻酸的检测。
[Abstract]:The separation and analysis of complex samples is one of the hot and difficult points in the field of analytical science in recent years. Based on the theory of traditional capillary electrophoresis (CE), a new capillary electrokinetic separation technique, CEkFN, was established in this paper, and it was combined with high resolution mass spectrometry (HRMS) to analyze and identify several typical complex samples. The paper is divided into seven chapters. The main contents are as follows: in Chapter 1, the definition and characteristics of complex samples, the characteristics and advantages of capillary electrophoresis and high resolution mass spectrometry are introduced. The theoretical basis and scheme design of the capillary electrokinetic separation technology established in this paper are described in detail. In the second chapter, 14 representative model compounds are selected and CEKF technique is used to establish semi-empirical model according to the change of signal intensity of model compounds with pH of matrix, and the calculation formula of PKA is deduced. The model can simultaneously determine PKA and conjecture the structure without complicated separation. In chapter 3, on the basis of PKA determination model, the main metabolites in wine were analyzed by using the established CEKF technique and Fourier transform ion cyclotron resonance Fourier transform ion cyclotron resonance, FTICR mass spectrometry. It is proved that CEKF has a high selectivity to sulfur compounds in wine under extremely acidic conditions. In chapter 4, the high selectivity of sulfur compounds by CEkF technique and the ultra-high resolution of FTICR / MS are used to analyze and identify the water-soluble organic compounds-organic sulfonic acid esters in secondary organic aerosols of complex samples. More than 500 kinds of organic sulfonic esters and nitroorganic sulfonic esters were identified in the biocombustible samples under extremely acidic conditions. In chapter 5, the synthetic CEKF technique is combined with ultrahigh resolution FTICR / MS and high field nuclear magnetic resonance (NMRs) to characterize the molecular level of natural organic matter, Suwannee River fulvic acid suwannee fulvic acid. This is the first time that electrophoretic technique is combined with NMR to study complex samples. In chapter 6, the simultaneous separation and detection of microcystin isomers in algae samples and the rapid detection of 尾 _ 2-agonist in urine were achieved by using the CE-ESI / MS technique and the field amplification amplified sample stacking, FASS technique of electric injection as the on-line enrichment method. Sensitive separation and detection. In chapter 7, the stability of amnesic shellfish-alginic acid was investigated by high performance liquid chromatography-quadruplex tandem mass spectrometry. Under the optimum chromatographic conditions, the determination of alginic acid in seawater and shellfish samples was successfully realized.
【学位授予单位】：福州大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：R917;O657.63

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