Quality Evaluation of Radix et Rhizoma Rhei Based on Gray Correlational Analysis and Functional Components / 世界科学技术-中医药现代化

Objective: To comprehensively evaluate the quality of Radix et Rhizoma Rhei based on gray correlationalanalysis and functional components, and to explore the difference of Radix et Rhizoma Rhei in different genuineproducing areas. Methods: HPLC was utilized to analyze 14 main compositions contained in the samples, includingemodin, rhein, chrysophanol, aloe-emodin, physcion, rheinoside, physcion glucoside, chrysophan, aloe-emodinglucoside, emodin methyl glycoside, sennoside, sennoside B, catechin and gallic acid. Then python 2.7 software wasemployed for gray correlation analysis of functional components closely related to the traditional efficacy of Radix et Rhizoma Rhei. Results: The qualities of Radix et Rhizoma Rhei grow in different areas were different. Tanggute Radix et Rhizoma Rhei grew in Tianzhu Gansu had the best effects of "expelling water retention and attacking the accumulation", and that grew in Yajiang Sichuan had the best effects of "clearing heat and removing toxin". Zhangye Radix et Rhizoma Rhei grew in Lixian Gansu had the best effect of"expelling stasis and unblocking the channels". Conclusion: Patternrecognition has broad prospects in the field of quality evaluation of traditional Chinese medicine. From the clinicalefficacy of traditional Chinese medicine, pattern recognition at the level of efficacy components can provide a new ideafor establishing a more complete and scientific quality evaluation system for traditional Chinese medicine.

Quality and quantity classification models of Fructus Amomi applying electronic nose with multiple mathematical statistics methods / 国际药学研究杂志

Objective Fructus Amomi(Sharen) is derived from the dry ripe fruit of Amomum villosum Lour., A.villosum Lour. var. xanthioides T.L. Wu et Senjen and A.longiligulate T.L.Wu, which is widely utilized for its clinic effects on digestive system. However, Fructus Amomi from different species and habitats, possessing different quality, is difficult to identify. In this study, we aim to develop a simple, rapid and reliable method for authentication of Fructus Amomi. Methods Twenty-five batches of samples of Fructus Amomi were collected and electronic nose was introduced into analyzing their odor with multiple mathematical statistics methods. Na?ve bayes network (NBN), radical basis function (RBF) and random forest (RF) were applied to establish different classifiers while BestFirst+CfsSubsetEval (BC) was used to screen the attributes for searching sensor array with higher contributions. Results Firstly, after attribute-screening via BC, the established discriminative models via NBN, RBF and RF could successfully identify genuine and non-genuine samples, presenting correct judging ratios of 78% and 84% through ten-fold cross validation and external test set validation, respectively. Besides, quantity predictive models were constructed as well. In case of content of bornyl acetate, one of the effective components in Fructus Amomi, values were higher than 3.5 mg/g and lower than 1.8 mg/g with sensor response of 0.04 and 0.03, respectively. Conclusion In this paper, quality discriminative model and quantity predictive model of Fructus Amomi were established via electronic nose and multiple mathematical statistics methods. It indicates that electronic nose could be a promising method for quality evaluation of Chinese material medica.

Identification of Sulfur Fumigated Chrysanthemi Flos with Electronic Nose Technology / 世界科学技术-中医药现代化

This study was aimed to identify Chrysanthemi Flosbefore and after sulfur fumigation based on its different odour by the electronic nose technology.It was expected to explore a new method for the Chrysanthemi Flos identification according to the odour.The electronic nose technology was used in the detection of peak response values of Chrysanthemi Flos on sensor array.The principal component analysis (PCA) and 10 machine learning (ML) ways were used in the analysis of response values and establishment of optimized identification models.The results showed that there was a significant difference in the odour between sulfur fumigated Chrysanthemi Flos and non-sulfur fumigated ones.The identification models were successful with high correct judge rate by PCA and 6 ML ways including BF Tree,J48 and Random Tree.It was concluded that the electronic nose technology can be used for the accurate identification of sulfur fumigated Chrysanthemi Flos and non-sulfur fumigated ones.The electronic nose technology combined with multiple ML methods can be introduced in the quality evaluation of Chrysanthemi Flos.It provided more ideas for the application of electronic nose in data mining for traditional Chinese medicine (TCM) studies.