Article ; Online: Stepped collisional energy MS(All) : an analytical approach for optimal MS/MS acquisition of complex mixture with diverse physicochemical properties.
Journal of mass spectrometry : JMS
2016 Volume 51, Issue 5, Page(s) 328–341
Abstract: 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 ...
| Abstract | 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 MS(All) (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 MS(All) 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 MS(All) 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 © 2016 John Wiley & Sons, Ltd. |
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| Language | English |
| Publishing date | 2016 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 1221763-3 |
| ISSN | 1096-9888 ; 1076-5174 |
| ISSN (online) | 1096-9888 |
| ISSN | 1076-5174 |
| DOI | 10.1002/jms.3751 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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