Metabolic Discrimination of Different Rhodiola Species Using 1H-NMR and GEP Combinational Chemometrics.

Rhodiola is widely consumed in traditional folk medicine and nutraceuticals. To establish a procedure for the hydrogen (1H)-NMR spectroscopic fingerprinting of secondary metabolites from three different Rhodiola species, the variation among three Rhodiola species were studied using 1H-NMR metabolomics combined with multivariate data analysis. Gene expression programming (GEP) was used to generate a formula to distinguish Rhodiola crenulata from two other Rhodiola species. Finally, HPLC was used to demonstrate the results. Same metabolites were compared by quantitative 1H-NMR (qNMR). Three Rhodiola species were clearly discriminated by 1H-NMR fingerprinting involved 22 nuclear magnetic signals of chemical constituents. y = d166 × 2 + C1 + d56 + d236 - d128 × C2 can be used to distinguish R. crenulata from two other Rhodiola species by GEP. The gallic acid concentration in R. crenulata was significantly higher than in the other. Rhodiola species as was the level of salidroside. R. crenulata also exhibited substantially higher levels of α-glucose. The fatty acid level in Rhodiola kirilowii was lower than the other species. These findings demonstrated that 1H-NMR fingerprinting combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and GEP can be used to distinguish different Rhodiola species and these methods were applicable and effective approaches for metabolic analysis, species differentiation, and quality assessment. In addition, gallic acid, salidroside, α-D-glucose, glycine, alanine, caffeic acid and tyrosol and are the discriminators.

[Resource investigation about Tibetan medicine Rhodiola kirilowii].

The investigation aims to better understand the resource status of Rhodiola kirilowii, analysis the suitable habitat of wild Rh. kirilowii and protect the wild resources of Rh. Kirilowii, promoting the sustainable utilization of Rh. kirilowii resources. In this paper, we investigated the wild resources of Rh. kirilowii in 16 counties of Sichuan, Qinghai, Gansu and Yunnan by means of investigation and sampling investigation combined with interview. The results showed that the population densities of wild Rh. kirilowii in 4 provinces were very different and the reserve of wild resources decreased gradually in many areas. According to the survey results, the current total reserve of Rh. kirilowii in four provinces was about 1 100 t. The reserve of wild Rh. kirilowii in Sichuan province was the largest. Simultaneously, the Rh. kirilowii had a certain ecological value. We found that a sand control base with planting Rh. kirilowii was set up in Hongyuan County of Sichuan Aba Tibetan and Qiang Autonomous Prefecture. The investigation provides a scientific basis for the development and sustainable utilization of Rh. kirilowii resources.

[Potential distribution of Rhodiola crenulata in Tibetan Plateau based on Maxent model].

Wildlife tending and artificial cultivation is an important way to protect the wild resources of Rhodiola crenulata. It is a study hotspot at present. The distribution information of R. crenulata was collected by query data and field survey, the ecological suitability regionalization was conducted based on maximum entropy model combine with ecological factors, including climate, soil and altitude. To provide the reference for production layout, suitable planting area and the selection of artificial planting base by studying the ecological suitability regionalization of R. crenulata. The potential distribution areas mainly concentrated in the easen Tibet, western Sichuan, southern Qinghai, and Gansu Gannan Tibetan Autonomous Prefecture, Yunnan Diqing Tibetan Autonomous Prefecture. There were 5 major environmental factors to have obvious influence on ecology suitability distributions of R. crenulata, including altitude (contribution rate of 61.8%), precipitation of warmest quarter (contribution rate of 19%), the coefficient of variation of precipitation seasonality (contribution rate of 4.7%), the SD of temperature seasonality (contribution rate of 4%), mean temperature of driest quarter (contribution rate of 2.5%). The AUCs of ROC curve were both above 0.9, indicating that the predictive results with the Maxent model were highly precise. The study of the ecological suitability regionalization of R. crenulata based on Maxent can provide a scientific basis for the selection of artificial planting base.