Investigation Of Information Processing Methods For Quality Analysis Of Traditional Chinese Medicine

In recent years, research on the methods of information processing for quality analysis of Traditional Chinese Medicine (TCM) has become an active area in the fields of Chemistry, Biology and Pharmacy, and gained increasing attention. With the development of experimental techniques like modern instrumental analysis, the data sets of chemical composition and drug effect of TCM are multiplying rapidly. Furthermore, they have indicated the characteristics of high dimension, small samples, multiple variables and strong interaction. Aimed at such complex analytical systems, the ideas and approaches in Chemoinformatics were introduced into the information processing of quality analysis for TCM. A variety of computing techniques, including artificial neural networks (ANN), wavelet transform (WT), genetic algorithms (GA), evolutionary learning, support vector machines (SVM) and visualization, were ingeniously integrated to deal with the challenging issue in the identification of TCM. The main contents of my thesis are listed as follows:(1) Aimed at the qualitative identification of TCM, the up-to-date technique in the area of machine learning, namely support vector machines, was incorporated with decision tree (DT) algorithm and extended to multi-class cases. Compared with traditional statistical pattern recognition paradigms and neural networks, the combined SVM-DT strategy can effectively overcome the drawback of ANN to over-fit and has powerful capability of generation. Its successful applications in the classification of geographic origins and quality grades for TCM have proved its great potential for the identification of high-dimensional small samples.(2) The QCAR investigation of TCM was further extended from the qualitative to quantitative level. The concept of quantitative structure-activity relationship (QSAR) in western medicines was introduced into the research of TCM, and a novel QCAR modeling method was further proposed. Through the integration of ANN, GA and evolutionary learning, the new strategy was successfully employed to the QCAR modeling of TCM. The results showed that itis superior to single ANN or GA approach in training accuracy, predicting accuracy and model reliability. Therefore it provides a new effective tool for the intelligent prediction of pharmacological activities of TCM.(3) Due to the increased quantity of analytical data for TCM and the difficulty of data interpretation, visualization technique was adopted to the measured analytical data processing. A novel analytical data visualization method was developed, in which the hidden chemical features were extracted using WT and kernel principal component analysis (kPCA), respectively, and then transformed into the virtual fingerprint by the two-dimensional grayscale images. In this way, the abstract data sets can be represented as computer-based images and the medicinal samples may be identified and appraised visually. The successful applications of this method in the data processing of chromatogram and spectrogram demonstrated its significant advantages over canonical identification approaches.