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首页> 外文期刊>Journal of mass spectrometry: JMS >Stepped collisional energy MSAll: an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties
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Stepped collisional energy MSAll: an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties

机译:阶梯式碰撞能量MSAll:一种具有多种理化性质的复杂混合物的最佳MS / MS采集的分析方法

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摘要

The analysis of complex mixtures is becoming increasingly important in various fields, such as nutrition, medicinal plants and metabolomics. The components contained in such complex mixtures are always characterized with diverse physiochemical properties that pose a major challenge during the optimization of various parameters using liquid chromatography-mass spectrometer (LC-MS). The parameter 'CE energy' that is normally set at a fixed value with a moderate range of CE spread during data-dependent acquisition (DDA) analysis, a prevalent approach for untargeted identification, often fails to generate sufficient MS/MS fragment ions for untargeted identification of components from complex mixtures. Here we developed a simple and generally applicable acquisition method named stepped MSAll (sMS(All)) in this study, aiming to obtain optimal MS/MS spectra for identification of chemically diverse compounds from complex mixtures. sMS(All) collects serial MSAll scans acquired at low CE to gradually ramped-up high CE values in a cycle that conventional DDA scans cannot afford. The resultant MS/MS spectra of each compound were compared and evaluated among serial MSAll scans, and the optimal spectra were used for identification. An untargeted data analysis strategy was then employed to analyze these optimal MS/MS spectra by searching common diagnostic ions and connecting the diagnostic ion families into a network via bridging components. This sMS(All)-based route enables identification of 71 natural products from a herbal preparation, whereas only 53 out of 71 compounds were identified using the classical DDA approach. Therefore, the sMS(All)-based approach is expected to find its wide applications for characterization of vastly diverse compounds with no priori knowledge from various complex mixtures. Copyright (C) 2016 John Wiley & Sons, Ltd.
机译:在营养,药用植物和代谢组学等各个领域,复杂混合物的分析变得越来越重要。此类复杂混合物中包含的组分始终具有多种理化特性,这在使用液相色谱-质谱仪(LC-MS)优化各种参数的过程中提出了重大挑战。参数“ CE能量”通常设置为固定值,并且在数据依赖型采集(DDA)分析过程中会以中等的CE扩展范围进行,这是一种非目标识别的普遍方法,通常无法为非目标识别生成足够的MS / MS碎片离子鉴定复杂混合物中的成分。在这里,我们在这项研究中开发了一种简单且通用的采集方法,称为阶梯式MSAll(sMS(All)),旨在获得最佳MS / MS光谱,用于鉴定复杂混合物中的化学多样性化合物。 sMS(All)收集在低CE下采集的串行MSAll扫描,以在传统DDA扫描无法承受的周期内逐渐提高高CE值。在串行MSAll扫描之间比较和评估每种化合物的合成MS / MS光谱,并使用最佳光谱进行鉴定。然后,通过搜索常见诊断离子并通过桥接组件将诊断离子家族连接到网络中,采用了非目标数据分析策略来分析这些最佳MS / MS光谱。这种基于sMS(All)的途径能够从草药中鉴定出71种天然产物,而使用经典DDA方法只能鉴定出71种化合物中的53种。因此,基于sMS(All)的方法有望在表征各种化合物的过程中获得广泛的应用,而无需掌握各种复杂混合物的先验知识。版权所有(C)2016 John Wiley&Sons,Ltd.

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