Simultaneously quantitative analysis of peptides and chemical components in Cervus and Cucumis polypeptide injection (Songmeile®) using reversed phase liquid chromatography-hydrophilic interaction liquid chromatography-tandem mass spectrometry.

作者: Wenjing Liu , Yan Cao , Yi Ren , Xia Xu , Liqun He

DOI: 10.1016/J.CHROMA.2019.460827

关键词: Tandem mass spectrometryChromatographyChemistryReversed-phase chromatographyHydrophilic interaction chromatographyCalibration curveQuantitative analysis (chemistry)OrbitrapAnalyteSelected reaction monitoring

摘要: Abstract Co-occurrences of peptides and chemical components are usually observed in complicated matrices. Notably, those traditional Chinese medicine prescriptions (TCMPs) contain both plant animal ingredients. It is still challenging to simultaneously monitor attributing their different liquid chromatographic (LC) mass spectrometric (MS) behaviors. Herein, efforts were made pursue an eligible approach enabling simultaneous determination a TCMP namely Cervus Cucumis polypeptide injection (CCPI, Songmeile®) that prepared from the acid hydrolytic peptide-enriched extract Sika deer (Cervus nippon Temminck) bone aqueous muskmelon (Cucumis melo L.) seeds. Reversed phase chromatography hydrophilic interaction serially connected (RPLC-HILIC) achieve comprehensive retention separation. Sensitive detection was accomplished with selected reaction monitoring (SRM) mode, multiply charged singly ion transitions defined for components, respectively. Inter-batch variations CCPI evaluated authentic compound-independent manner. In particular, online energy-resolved MS proposed gain optimal parameters five targeted after peptidome profiled using nanoLC-LTQ Orbitrap Velos Pro MS. A so-called universal metabolome standard (UMS) sample built calibration curve construction subsequently applied acquire quasi-contents all 31 analytes, including 26 ten batches (CCPI1–CCPI10). The quantitative dataset revealed mild fluctuation quasi-content profiles analytes-of-interest within batches. More importantly, RPLC-HILIC–SRM promising method fully address demands measurement matrices, it might be robust analytical tool in-depth quality evaluation as well other TCMPs.

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