Zhou Fei, Zhao Yajing, Peng Jiyu, Jiang Yirong, Li Maiquan, Jiang Yuan, Lu Baiyi
College of Biosystems Engineering and Food Science, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang R & D Centre for Food Technology and Equipment, Key Laboratory for Agro-Food Risk Assessment of Ministry of Agriculture, Zhejiang University, Hangzhou, 310058, China.
Phytochem Anal. 2017 Jul;28(4):305-315. doi: 10.1002/pca.2677. Epub 2017 Feb 24.
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.
To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites.
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.
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.
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.
桂花被用作民间药物以及茶、饮料和食品的添加剂。不同地理来源的桂花代谢产物在一定程度上存在差异。色谱法和质谱法结合多变量分析方法为鉴别桂花的产地提供了一种途径。
用已鉴定的代谢产物鉴别不同产地的金桂花朵。
采用气相色谱 - 质谱联用（GC-MS）和超高效液相色谱 - 二极管阵列检测（UPLC-PDA）分析金桂花朵中的代谢产物（共150个样品）。应用主成分分析（PCA）、类相关软独立建模分析（SIMCA）和随机森林（RF）分析对GC-MS和UPLC-PDA数据进行分组。
GC-MS鉴定出所有样品共有的32种化合物，而UPLC-PDA/QTOF-MS鉴定出16种共有化合物。UPLC-PDA数据的PCA比GC-MS数据的PCA产生了更好的聚类效果。通过PCA载荷选择了10种代谢产物（6种来自GC-MS，4种来自UPLC-PDA）作为鉴别有效化合物。使用SIMCA和RF分析建立分类模型，基于四种有效化合物（咖啡酸衍生物、毛蕊花糖苷、女贞苷和化合物15）的RF模型效果更好，校正集的分类率为100%，预测集的分类率为97.8%。
GC-MS和UPLC-PDA结合多变量分析方法可以鉴别金桂花朵的产地。版权所有© 2017约翰威立父子有限公司。
