Shengyun Dai, Yuqi Wang, Fei Wang, Xiaodan Mei, Jiayu Zhang
School of Chinese Pharmacy, Beijing University of Chinese Medicine Beijing 102488 China.
National Institute of Food and Drug Control Beijing 100050 China.
RSC Adv. 2019 Oct 2;9(53):31150-31161. doi: 10.1039/c9ra05032a. eCollection 2019 Sep 26.
In the current work, Flos (FLJ) was selected as a model Chinese herbal medicine (CHM) and a protocol was proposed for the rapid detection of sulfur-fumigated (SF) CHMs. A multiple metabonomics analysis was conducted using HPLC, NIR spectroscopy and a UHPLC-LTQ-Orbitrap mass spectrometer. First, the group discriminatory potential of each technique was respectively investigated based on PCA. Then, the effect of mid-level metabonomics data fusion on sample spatial distribution was evaluated based on data obtained using the above three technologies. Furthermore, based on the acquired HRMS data, 76 markers discriminating SF from non-sulfur-fumigated (NSF) CHMs were observed and 49 of them were eventually characterized. Moreover, NIR absorptions of 18 sulfur-containing markers were identified to be in close correlation with the discriminatory NIR wavebands. In conclusion, the proposed protocol based on integrative metabonomics analysis that we established for the rapid detection and mechanistic explanation of the sulfur fumigation of CHMs was able to achieve variable selection, enhance group separation and reveal the intrinsic mechanism of the sulfur fumigation of CHMs.
在当前工作中，选择了花类药材（FLJ）作为模型中药，并提出了一种快速检测硫熏中药的方案。使用高效液相色谱法（HPLC）、近红外光谱法（NIR）和超高效液相色谱-线性离子阱-轨道阱质谱仪进行了多组学分析。首先，基于主成分分析（PCA）分别研究了每种技术的组间区分潜力。然后，基于上述三种技术获得的数据，评估了中级组学数据融合对样品空间分布的影响。此外，基于获得的高分辨质谱（HRMS）数据，观察到76个区分硫熏与非硫熏中药的标志物，最终鉴定出其中49个。此外，还确定了18个含硫标志物的近红外吸收与具有区分性的近红外波段密切相关。总之，我们建立的基于综合组学分析的方案用于中药硫熏的快速检测和机理解释，能够实现变量选择、增强组间分离并揭示中药硫熏的内在机制。
