State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China.
Phytochem Anal. 2019 Jul;30(4):437-446. doi: 10.1002/pca.2826. Epub 2019 Feb 28.
As sources of Rhizoma Paridis are facing shortages, utilising the aerial parts of Paris polyphylla has emerged as a promising additional source. However, the components in the aerial parts still need to be explored, and it is difficult to distinguish the aerial parts of P. polyphylla Smith var. yunnanensis (PPY) and P. polyphylla var. chinensis (PPC), two varieties of P. polyphylla.
This study aimed to establish a comprehensive platform to characterise steroid saponins from the aerial parts of PPY and PPC and to discriminate these two varieties.
A dereplication approach and ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) analysis were used for the characterisation of steroidal saponins in the aerial parts of PPY and PPC. Multivariate statistical analysis was performed to differentiate these two varieties and screen discriminant variables. In addition, a genetic algorithm-optimised for support vector machines (GA-SVM) model was developed to predict P. polyphylla samples. The distribution of steroidal saponins in PPY and PPC was visualised by a heatmap.
A total of 102 compounds were characterised from the aerial parts of PPY and PPC by dereplication. A clear separation of PPY and PPC was achieved, and 35 saponins were screened as marker compounds. The established GA-SVM model showed excellent prediction performance with a prediction accuracy of 100%.
Many steroid saponins that have been reported in Rhizoma Paridis also exist in the aerial parts of P. polyphylla. Samples from the aerial parts of PPY and PPC could be discriminated using our platform.
由于重楼的来源短缺,利用云南重楼和中华重楼的地上部分作为潜在的替代资源已成为一种有前途的方法。然而,地上部分的成分仍需要进一步研究,而且云南重楼和中华重楼这两种重楼品种的地上部分很难区分。
本研究旨在建立一个综合平台,以鉴定云南重楼和中华重楼地上部分的甾体皂苷成分,并对这两个品种进行区分。
采用去重法和超高效液相色谱-四极杆飞行时间质谱(UHPLC-QTOF-MS)分析鉴定云南重楼和中华重楼地上部分的甾体皂苷成分。采用多元统计分析方法对这两个品种进行区分,并筛选出判别变量。此外,还建立了遗传算法-支持向量机(GA-SVM)模型来预测重楼样品。采用热图可视化甾体皂苷在云南重楼和中华重楼地上部分的分布。
通过去重法共鉴定出云南重楼和中华重楼地上部分的 102 种化合物。云南重楼和中华重楼得到了很好的区分,筛选出 35 种皂苷作为标记化合物。建立的 GA-SVM 模型具有出色的预测性能,预测准确率为 100%。
许多在重楼中报道的甾体皂苷也存在于云南重楼的地上部分。本研究建立的平台可以区分云南重楼和中华重楼地上部分的样品。