[Quality evaluation of commercial Ginseng Radix et Rhizoma Rubra based on multi-component quantitative analysis].

To comprehensively evaluate the quality of commercial Ginseng Radix et Rhizoma Rubra, 43 batches of commercial Ginseng Radix et Rhizoma Rubra were collected to determine the content of nine ginsenosides Rg_1, Re, Rb_1, Rk_3, Rh_4, 20(S)-Rg_3, 20(R)-Rg_3, Rk_1, and Rg_5 by high performance liquid chromatography(HPLC). The quality of the commercial Ginseng Radix et Rhizoma Rubra was evaluated by correlation analysis, principal component analysis, factor analysis, analysis of variance(ANOVA), and cluster heatmap analysis. The content determination indicated that the content of common ginsenosides in commercial Ginseng Radix et Rhizoma Rubra were higher while that of rare ginsenosides were lower. Multivariate statistical analysis revealed that ginsenosides Rg_1 and Rb_1 were significantly positively correlated with rare ginsenosides, and Rg_1, Rb_1 and rare ginsenosides played an important role in evaluating the quality of commercial Ginseng Radix et Rhizoma Rubra. In combination with the processing principle and current quality situation of Ginseng Radix et Rhizoma Rubra, it is recommended to improve the content limit of Rb_1 in the existing quality standards.

[Analysis of chemical constituents in Shenmai Injection by LC-Q-TOF-MS and LC-IT-MS].

The chemical constituents in Shenmai Injection(SMI) were qualitatively analyzed by using liquid chromatography/quadrupole time-of-flight mass spectrometry(LC-Q-TOF-MS) and liquid chromatography-ion trap-mass spectrometry(LC-IT-MS). The analysis was performed on an Agilent Zorbax SB-C_(18)(4.6 mm×250 mm, 5 µm) and gradient elution was carried out with 0.05% formic acid solution-acetonitrile as mobile phase at a flow rate of 0.6 mL·min~(-1) and a column temperature of 30 ℃. Mass spectrometry data of the components in SMI were collected in negative ion mode. The structures of components were speculated and identified by analyzing mass spectrometry data, comparing with standards, and referring to related literature. A total of 64 components in SMI were estimated, and the structures were confirmed in 16 of them by comparison with standards. Fifty-six compounds derived from Ginseng Radix et Rhizoma Rubra included 34 protopanaxadiol ginsenosides, 19 protopanaxatriol ginsenosides, 1 oleanane ginsenosides and 2 other glycosides. Eight compounds derived from Ophiopogonis Radix included 7 steroidal saponins, and 1 monoterpene glycoside. The results of this study would provide an important theoretical basis for the improvement of the quality control standards and the discovery of effective constituents in SMI.

An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning.

Objective: To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. Methods: A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. Results: An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. Conclusion: The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.