Qualitative analysis and differentiation of ginkgo cultivars based on UHPLC-QTOF-MS/MS with the characteristic ion and neutral loss strategy combined with chemometric methods
The identification of chemical constituents can assist the discovery of active ingredients and can differentiate herbs with multiple cultivars. In this study, a diagnostic ion and neutral loss filtering strategy was developed for the qualitative analysis of ginkgo leaf. The strategy is based on an u...
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Veröffentlicht in: | Journal of pharmaceutical and biomedical analysis 2022-03, Vol.211, p.114595-114595, Article 114595 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The identification of chemical constituents can assist the discovery of active ingredients and can differentiate herbs with multiple cultivars. In this study, a diagnostic ion and neutral loss filtering strategy was developed for the qualitative analysis of ginkgo leaf. The strategy is based on an ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. A large number of (110) of GL compounds were identified, including 8 potentially novel compounds and 42 previously unreported GL constituents. Moreover, 64 available compounds in 48 GL cultivars were analyzed via a combined multicomponent quantitative analysis and statistical analysis. The distribution of the 64 compounds among different cultivars was clarified in a principal component analysis and hot map visualization. Via a variable-importance-for-prediction score analysis, ten main differential compounds were found among the different cultivars. Collectively, these results indicated the usefulness of our approach in chemical profiling and discrimination of herbs with multiple botanical origins. This strategy can also help chemists rapidly identify novel compounds from a complex matrix.
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•GL was analyzed using the UHPLC–QTOF-MS/MS and a diagnostic ion/neutral loss strategy.•Quantitative data of 64 available compounds from different cultivars were analyzed.•The quantitative data were statistically analyzed by chemometric methods. |
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ISSN: | 0731-7085 1873-264X |
DOI: | 10.1016/j.jpba.2022.114595 |