Simultaneous Determination of 25 Ginsenosides by UPLC-HRMS via Quantitative Analysis of Multicomponents by Single Marker.
Xiujuan JiaChenxing HuXuepeng ZhuYe YuanYifa ZhouPublished in: International journal of analytical chemistry (2021)
A method using UPLC-HRMS has been developed for a rapid, simultaneous qualitative and quantitative analysis of twenty-five ginsenosides. Chromatographic separation was achieved on a C18 analytical column with an elution gradient comprising 0.1% aqueous formate/acetonitrile as the mobile phase. HRMS detection acquired full mass data for quantification and fullms-ddms2 (i.e., data-dependent scan mode) yielded product ion spectra for identification. Furthermore, quantitative analysis of multiginsenosides by single marker (QAMS) was developed and validated using a relative correction factor. Under optimal conditions, we could simultaneously separate eight groups of isomers of the 25 ginsenosides. Good linearity was observed over the validated concentration range for each analyte (r 2 > 0.9924), showing excellent sensitivity (LODs, 0.003-0.349 ng/mL) and lower limit quantification (LOQs, 0.015-1.163 ng/mL). The LC-MS external standard method (ESM) and QAMS were compared and successfully applied to analyze the ginsenoside content from Panax ginseng roots. Overall, our UPLC-HRMS/QAMS approach provides high precision, stability, and reproducibility and can be used for high-throughput analysis of complex ginsenosides and quantitative analysis of multiple components and quality control of traditional Chinese medicines (TCM).
Keyphrases
- simultaneous determination
- liquid chromatography
- high resolution mass spectrometry
- tandem mass spectrometry
- liquid chromatography tandem mass spectrometry
- mass spectrometry
- ultra high performance liquid chromatography
- high performance liquid chromatography
- high resolution
- solid phase extraction
- quality control
- high throughput
- electronic health record
- computed tomography
- systematic review
- loop mediated isothermal amplification
- big data
- magnetic resonance
- machine learning
- single cell
- deep learning