Fuzzy clustering evaluation of the discrimination power of UV–Vis and (±) ESI-MS detection system in individual or coupled RPLC for characterization of Ginkgo Biloba standardized extracts

AbstractAim mination power evaluation of UV–Vis and (±) electrospray ionization/mass spectrometric techniques, (ESI-MS) individually considered or coupled as detectors to reversed phase liquid chromatography (RPLC) in the characterization of Ginkgo Biloba standardized extracts, is used in herbal medicines and/or dietary supplements with the help of Fuzzy hierarchical clustering (FHC). mental een batches of Ginkgo Biloba commercially available standardized extracts from seven manufacturers were measured during experiments. All extracts were within the criteria of the official monograph dedicated to dried refined and quantified Ginkgo extracts, in the European Pharmacopoeia. UV–Vis and (±) ESI-MS spectra of the bulk standardized extracts in methanol were acquired. Additionally, an RPLC separation based on a simple gradient elution profile was applied to the standardized extracts. Detection was made through monitoring UV absorption at 220 nm wavelength or the total ion current (TIC) produced through (±) ESI-MS analysis. FHC was applied to raw, centered and scaled data sets, for evaluating the discrimination power of the method with respect to the origins of the extracts and to the batch to batch variability. s scrimination power increases with the increase of the intrinsic selectivity of the spectral technique being used: UV–Vis<MS(−)<MS(+), but it is strongly sensitive to chemometric transformation of data. Comparison with cluster analysis (CA) and principal components analysis (PCA) indicates that the FHC algorithm produces better classification. However, PCA and CA may be successfully applied to discriminate between the manufacturing sources of the standardized extracts, and at some extent, to monitor the inter-batch variability. Although the chromatographic dimension sensibly contributes to the discrimination power, spectral MS data may be used as the lone powerful holistic alternative in characterization of standardized Ginkgo Biloba extracts.