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A targeted strategy to analyze untargeted mass spectral data: Rapid chemical profiling of Scutellaria baicalensis using ultra-high performance liquid chromatography coupled with hybrid quadrupole orbitrap mass spectrometry and key ion filtering
Ist Teil von
Journal of Chromatography A, 2016-04, Vol.1441, p.83-95
Ort / Verlag
Netherlands: Elsevier B.V
Erscheinungsjahr
2016
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
•A key ion filter (KIF) strategy was developed to process untargeted LC/MS data.•The strategy was realized by using UHPLC/orbitrap-MS to analyze S. baicalensis.•High-resolution MS and MS/MS data were collected automatically.•Diagnostic ions from MS/MS spectra were extracted to recognize key substructures.•A total of 132 compounds were identified from S. baicalensis in a 20-min run.
Structural identification of natural products by tandem mass spectrometry requires laborious spectral analysis. Herein, we report a targeted post-acquisition data processing strategy, key ion filtering (KIF), to analyze untargeted mass spectral data. This strategy includes four steps: (1) untargeted data acquisition by ultra-high performance liquid chromatography coupled with hybrid quadrupole orbitrap mass spectrometry (UHPLC/orbitrap-MS); (2) construction of a key ion database according to diagnostic MS/MS fragmentations and conservative substructures of natural compounds; (3) high-resolution key ion filtering of the acquired data to recognize substructures; and (4) structural identification of target compounds by analyzing their MS/MS spectra. The herbal medicine Huang-Qin (Scutellaria baicalensis Georgi) was used to illustrate this strategy. Its extract was separated within 20min on a C18 column (1.8μm, 2.1×150mm) eluted with acetonitrile, methanol, and water containing 0.1% formic acid. The compounds were detected in the (−)-ESI mode, and their MS/MS spectra were recorded in the untargeted manner. Key ions were then filtered from the LC/MS data to recognize flavones, flavanones, O-/C-glycosides, and phenylethanoid glycosides. Finally, a total of 132 compounds were identified from Huang-Qin, and 59 of them were reported for the first time. This study provides an efficient data processing strategy to rapidly profile the chemical constituents of complicated herbal extracts.