A study of qualitative and quantitative component analysis for Chinese proprietary medicines

Pao Yue-kong Library Electronic Theses Database

A study of qualitative and quantitative component analysis for Chinese proprietary medicines

 

Author: Wong, Siu-kay
Title: A study of qualitative and quantitative component analysis for Chinese proprietary medicines
Degree: M.Sc.
Year: 1999
Subject: Medicine, Chinese
Herbs -- Therapeutic use
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Applied Mathematics
Pages: iv, 74 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1463706
URI: http://theses.lib.polyu.edu.hk/handle/200/1506
Abstract: It is well known that traditional Chinese medicines have been used for over 5000 years. For convenience, more and more traditional Chinese medicines are now being manufactured and sold in the form of proprietary medicines recently. According to the information from the Department of Health, there are about 4000 types of Chinese proprietary medicines available in local markets. Over 80% of them are manufactured in the Mainland China. In order to implement regulatory control of these proprietary medicines, component analysis are required. However, component analysis of proprietary medicines is a challenging task and may involve complicated analytical procedures. In this project, a simple chromatographic pattern recognition technique was developed for the qualitative and quantitative component analysis of Chinese proprietary medicines. As a result, the ingredient herbs can be identified and their respective quantity in the medicinal samples can be estimated based on the chromatographic data of the Chinese proprietary medicines. Hence, a rapid and simple component analysis can be conducted without using complicated analytical procedures. A typical chromatogram consists of a profile of peaks characterized with retention times and respective peak areas. In the pattern recognition process, characteristic peaks of target herbs were compared with peaks of the sample chromatogram one by one. The relative peak area percent of those matched characteristic peaks were added up to give a matching factor. This factor was then used as a similarity index for the pattern recognition process in the study. At here, no advanced theory or complicated mathematical manipulation are involved as with other mathematical techniques such as factor analysis, numerical analysis, orthogonal projection, stepwise regression, Simplex optimization and neural network. These techniques had been reported for the determination of constituent components and their concentrations in a mixture based on chromatographic data. Moreover, a weighted least square optimization technique was introduced to estimate the quantity of the target herbs in the samples. Finally, the results of the proposed pattern recognition technique were compared with results obtained by other techniques including cluster analysis, SISCOM and ASES/MS pattern searching algorithms. The results indicated that the proposed matching factor method was much better and the mathematics involved was the simplest.

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