Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC–MS and UPLC‐PDA Combined with Multivariable Analysis Methods. Issue 4 (24th February 2017) - Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi
Abstract: Introduction: Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. Objective: To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. Methods: GC–MS and UPLC‐PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC–MS and UPLC‐PDA data. Results: GC–MS identified 32 compounds common to all samples while UPLC–PDA/QTOF‐MS identified 16 common compounds. PCA of the UPLC‐PDA data generated a better clustering than PCA of the GC–MS data. Ten metabolites (six from GC–MS and four from UPLC‐PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. Conclusions: GC–MS and UPLC‐PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Abstract : PCA was used to qualitatively discriminate the origins, however, SIMCA and RF were used to make classification models. Six effective compounds from GC‐MS and four effective compounds from UPLC‐PDA for discrimination were obtained by the PCA loadings. The RF model based on the four effective compounds from UPLC‐PDA data obtained good result to differentiate the origins.