## 3 Results and Discussion
### Discrimination of PJ, PM, and PZ by a Multivariate Statistical Analysis
PZ, commonly known as ginger ginseng or Myanmar ginseng, is indigenous to Yunnan province in the Southwest of China [13], and has also been reported to wildly grow at the Par Moe Ne Water Spring area in Taung-gyi, Shan State, Myanmar, which has an altitude of 1500 m above sea level. Few previous studies have focused on the comprehensive chemical compositions and determination of ginensosides in PZ [26]. In the present study, for the first time, the chemical compositions of this herb were screened and identified by UHPLC-Q-Exactive/HRMS, and the content of 16 major ginensoides was determined. These results are beneficial for the development and quality assessment of PZ. In conclusion, an integrated strategy to comprehensively identify the chemical composition and simultaneously quantify 16 ginensodies in the three _Panax_ herbs was successfully established. After optimization of the conditions, a total of 101 ginensosides were detected and tentatively identified using UHPLC-Q-Exactive/HRMS, including 82 from PJ, 78 from PM, and 67 from PZ. Among these compounds, 22 were unambiguously confirmed by comparing their retention times and mass spectra with those of reference ginensoides. The quantitative analysis was implemented using a reliable and practical HPLC-ESI-MS/MS method using MRM mode. The validation of the methodology showed favorable levels of LOD, LOQ, linearity, precision, repeatability, stability, and recovery. Finally, the PLS-DA and OPLS-DA results, based on the quantitative data, displayed a significant difference in ginensoside content between the three ginseng-drugs. G-Rf, G-F3, and CS-IV were the three most characteristic components that can distinguish between the three herbs. The integrated LC-MS-basedstrategy combined with a multivariate data analysis could not only achieve a rapid, accurate, and comprehensive qualitative and quantitative analysis of the complex ginseng but also enable us to discriminate between PJ, PM, and PZ, which provides valuable references for the quality assessment and control of traditional Chinese medicines (TCMs). Figure 6: The PLS-DA (partial least squares–discriminate analysis) score scatter plot (**A-1**), the loading scatter plot (**A-2**), the chance permutation test to validate the occurrence or absence of over-fitting of the PLS-DA (partial least squares–discriminate analysis) model (**A-3**), the VIP (variable importance in the projection) values of the 16 saponins (**A-4**), the OPLS-DA (orthogonal partial least squares–discriminant analysis) score scatter plot based on a pairwise comparison (**B-1**, **C-1**, **D-1**), and the VIP values of the 16 saponins (**B-2**, **C-2**, **D-2**). **Author Contributions:** [PERSON] and [PERSON] designed the study. [PERSON], [PERSON], [PERSON] processed the data, performed the analyses and analyzed the results, and wrote the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] edited the manuscript. All authors read and approved the final version of the manuscript. **Funding:** The work was supported by grants from the National Natural Science Foundation of China (No. 81274013, No. 81473315), the CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2016-IZM-3-015), the Key Projects in the National Science and Technology Pillar Program (NO. 2011 BAI07B08). **Conflicts of Interest:** The authors declare no conflict of interest.