Determination of total ginsenosides in ginseng extracts using charged aerosol detection with post-column compensation of the gradient

AIM: Variation in structure-related components in plant products prompted the trend to establish methods, using multiple or total analog analysis, for their effective quality control. However, the general use of routine quality control is restricted by the limited availability of reference substances. Using an easily available single marker as a reference standard to determine multiple or total analogs should be a practical option. METHOD: In this study, the Ultra-HPLC method was used for the baseline separation of the main components in ginseng extracts. Using a plant chemical component database, ginsenosides in ginseng extracts were identified by Ultra-HPLC-MS analysis. The charged aerosol detection(CAD) system with post-column compensation of the gradient generates a similar response for identical amounts of different analytes, and thus, the content of each ginsenoside in ginseng extracts was determined by comparing the analyte peak area with the reference standard(determination of total analogs by single marker, DTSM). The total ginsenoside content was determined by the summation of reference standard and other ginsenoside components. RESULTS: The results showed that DTSM approaches were available for the determination of total ginsenosides in a high purity ginseng extract because of the removal of impurities. In contrast, DTSM approaches might be suitable for determination of multiple ginsenosides without interference from impurities in the crude ginseng extract. CONCLUSION: Future practical studies similar to the present study should be conducted to verify that DTSM approaches based on CAD with post-column inverse gradient for uniform response are ideal for the quality control of plant products.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.