Ultrahigh pressure liquid chromatography-atmospheric pressure photoionization-tandem mass spectrometry for the determination of polyphenolic profiles in the characterization and classification of cranberry-based pharmaceutical preparations and natural extracts

作者：Parets Lidia; Alechaga Elida; Nunez Oscar^*; Saurina Javier; Hernandez Cassou Santiago; Puignou Lluis

来源：Analytical Methods, 2016, 8(22): 4363-4378.

DOI：10.1039/c6ay00929h

Ultrahigh pressure liquid chromatography-atmospheric pressure photoionization-tandem mass spectrometry (UHPLC-APPI-MS/MS) was applied to the analysis and authentication of fruit-based products and pharmaceutical preparations. Two sub-2 mu m C18 reversed-phase columns, Syncronis (100 x 2.1 mm, 1.7 mu m) and Hypersil Gold (50 x 2.1 mm, 1.9 mu m), were proposed under gradient elution with 0.1% formic acid aqueous solution and methanol mobile phases for the determination of 29 polyphenols, allowing us to obtain polyphenolic profiles in less than 13.5 and 23.5 min, respectively. Several atmospheric pressure ionization (API) sources (H-ESI, APCI, and APPI) were compared. For dopantassisted APPI, four organic solvents, toluene, acetone, chlorobenzene and anisole, were evaluated as dopants. Both H-ESI and acetone-assisted APPI were selected as the best ionization sources for the analysis of targeted polyphenols. Acceptable sensitivity (LOD values down to 0.5 mu g kg(-1) in the best of cases), linearity (r(2) higher than 0.995) and good precision (RSD values lower than 15.1%) and trueness (relative errors lower than 10.2%) were obtained using both UHPLC-API-MS/MS methods. A simple extraction procedure, consisting of sample sonication with acetone/water/hydrochloric acid (70 : 29.9 : 0.1 v/v/v) and centrifugation, was used. The proposed UHPLC-ESI-MS/MS and UHPLC-APPI-MS/MS methods with both C18 reversed-phase columns were then applied to the analysis of 32 grapebased and cranberry-based natural products and pharmaceutical preparations. Polyphenolic profile data were then analyzed by principal component analysis (PCA) to extract information on the most significant data contributing to the classification of natural extracts according to the type of fruit.

• 出版日期2016