[Enhancing effects of chemical permeation enhancer propylene glycol and ultrasonic technology on transdermal permeation of Baimai Ointment].

This study aimed to improve the transdermal permeation quantity of Baimai Ointment by investigating the enhancing effects of physical and chemical permeation promoting methods on transdermal permeation of Baimai Ointment. The improved Franz diffusion cell method was used for in vitro transdermal experiment. The abdominal skin of mice was used, and the skin was treated with 3% propylene glycol in the chemical enhancement group. Ultrasonic technology was introduced in the physical enhancement group. The conditions of ultrasonic technology were optimized by single factor trial. Taking Q_(EF) and ER as the indexes of penetration promotion performance, the enhancing effects of the two methods were compared. The results showed that the promotion performance of 3% propylene glycol for ammonium glycyrrhizinate, nardosinone and curcumin of the chemical enhancement group were 1.74, 1.60, and 3.73 times higher than those of the blank group, respectively. The overall permeation efficiency of the Baimai Ointment was significantly improved. The comprehensive promoting effect on each component was curcumin>ammonium glycyrrhizinate>nardosinone. In the physical enhancement group, the penetration promoting effect of ultrasonic power 1.0 W was better than that of 2.0 W and 0.5 W, ultrasonic time 5 min was better than 3 min and 8 min, and the ultrasonic frequency 1 MHz was better than 3 MHz. Therefore, the optimal ultrasonic condition was 1.0 W-5 min-1 MHz. Under this condition, in terms of the transdermal permeation for ammonium glycyrrhizinate, the Q_(EF) and ER of the ultrasonic technology were better than those of 3% propylene glycol. In terms of the transdermal permeation for nardosinone and curcumin, the QEF and ER of 3% propylene glycol were better than those of the ultrasonic technology. Therefore, 3% propylene glycol combined with ultrasonic technology can be used to promote permeation of Baimai Ointment that contains both water-soluble and fat-soluble components in the clinical application. This study provides a theoretical basis for the clinical application of Baimai Ointment and other transdermal preparations.

[Rancidness of Armeniacae Semen Amarum involving Bianzhuang Lunzhi].

This article aims to compare the qualities of Armeniacae Semen Amarum before and after rancidness, in order to study the rancidness of Armeniacae Semen Amarum. In the experiment, content of fatty oil, acid value and peroxide value were determined before and after rancidness,respectively. Meanwhile, HPLC, GC-MS were utilized to analyze laetrile and fatty acid components. Besides, colorimeter and e-nose were introduced to quantify and compare "color and odor". A correlation analysis was conducted on the above results. The results showed that color of post-rancidness Armeniacae Semen Amarum changed from yellow to brown, with sour and lower content of laetrile. On the contrary, acid and peroxide values increased significantly, with changes in fatty acid component. There was a considerable correlation between appearance characteristics and changes in internal quality. The "sensory analysis-quality identification system" can provide a certain scientific basis for prediction of the content of chemical components in traditional Chinese medicine, preliminary judgment of quality of traditional Chinese medicine and real-time quality monitoring, which offers us novel ideas and reference for storage principles of traditional Chinese medicines of "pre-event prediction, during-event intervention and post-event identification".

Is it possible to rapidly and noninvasively identify different plants from Asteraceae using electronic nose with multiple mathematical algorithms?

Many plants originating from the Asteraceae family are applied as herbal medicines and also beverage ingredients in Asian areas, particularly in China. However, they may be confused due to their similar odor, especially when ground into powder, losing their typical macroscopic characteristics. In this paper, 11 different multiple mathematical algorithms, which are commonly used in data processing, were utilized and compared to analyze the electronic nose (E-nose) response signals of different plants from Asteraceae family. Results demonstrate that three-dimensional plot scatter figure of principal component analysis with less extracted components could offer the identification results more visually; simultaneously, all nine kinds of artificial neural network could give classification accuracies at 100%. This paper presents a rapid, accurate, and effective method to distinguish Asteraceae plants based on their response signals in E-nose. It also gives insights to further studies, such as to find unique sensors that are more sensitive and exclusive to volatile components in Chinese herbal medicines and to improve the identification ability of E-nose. Screening sensors made by other novel materials would be also an interesting way to improve identification capability of E-nose.

Identification of chinese herbal medicines from zingiberaceae family using feature extraction and cascade classifier based on response signals from e-nose.

Identification of Chinese herbal medicines (CHMs) by human experience is often inaccurate because individual ability and external factors may influence the outcome. However, it might be promising to employ an electronic nose (E-nose) to identify them. This paper presents a rapid and reliable method for identification of ten different species of CHMs from Zingiberaceae family based on their response signals from E-nose. Ten Zingiberaceae CHMs were measured and their maximum response values were analyzed by principal component analysis (PCA). Result shows that E Zhu (Curcuma phaeocaulis Val.) and Yi Zhi (Alpinia oxyphylla Miq.) could not be distinguished completely by PCA. Two solutions were proposed: (i) using BestFirst+CfsSubsetEval (BC) method to extract more discriminative features to select sensors with higher contribution rate and remove the redundant signals; (ii) employing a novel cascade classifier with two stages to enhance the distinguishing-positive rate (DPR). Based on these strategies, six features were extracted and used in different stages of the cascade classifier with higher DPRs.