| In recent years, many modern instrumental analysis methods have been used in identification and qualification of herbal medicine. Infrared (IR) spectroscopy has an important use in qualitative and quantitative analyses of herbal medicine. Near-infrared spectroscopy (NIRS) technique has become a new method of qualitative identification and quantitative analysis of traditional Chinese medicine as a simple, rapid, undestroyed method. Artificial neural networks (ANNs) have recently gained attention as fast and flexible methods to quality control of traditional medicine. ANNs can be trained to recognize patterns by building nonlinear models. The models can be used to generalize their conclusions and to predict patterns not being previously encountered.Rhubarb is a well known traditional medicinal material used in China as a cathartic and a promoter of gastric health. In the Chinese pharmacopoeia there are only three recorded species, namely, R. palmatum, R. tanguticum and R. officinale, and these are referred to as official rhubarbs. Besides the official rhubarbs, radix and rhizome of other species within the genus Rheum are often used in folkloric medicine in various minority regions. However, the purgative activity of some unofficial rhubarbs is much lower than those of the official rhubarbs and some may even cause stomach ache. To ensure the safety and curative effectiveness of rhubarb in clinical practice, it is important to distinguish between official and unofficial rhubarbs. In the second chapter of the present study, combination of multilayer perceptron neural networks (MLPNNs) and NIR spectroscopy was attempted to identify official and unofficial rhubarbs. The MLPNNs were trained with back propagation (BP), delta-bar-delta (DBD), and quick propagation (QP) algorithms, respectively. The MLPNNs trained with the DBD algorithm achieved the highest recognition accuracy 94.2%, and QP algorithm and BP algorithm achieved the same recognition accuracy 92.3%. The purpose of this work is to provide a comparative study of various training algorithms for MLPNNs for identification of official and unofficial rhubarbs.Along with the decrease of wild official rhubarbs and the discovery of other pharmacological activities except for purgative activity, the studies on chemical constituents of rhubarbs have been paid more and more attention. The clinical application of rhubarbs is not only a medicine with purgative activity but with many applications. The chemical constituents of genus Rheum were confirmed being more than 160 species, which were mainly divided into anthracene derivatives, stilbenes, phenylbutanone glucosides, tannins, naphthalene derivatives and chromone derivatives. In the third chapter of the present study, combination of multilayer perceptron neural networks (MLPNNs) and NIR spectroscopy was attempted to develop a method for determining the chemical constituents anthraquinones, anthranones, stilbenes, tannins and related compounds. The MLPNNs were trained with back propagation (BP), delta-bar-delta (DBD), and quick propagation (QP) algorithms, respectively. On the basis of selection of parameters, the best calibration models were developed to correlate the spectra and the values determined by wet chemical analytical method using three different MLPNNs algorithms. Among the three ANN algorithms, the best models were QP neural network when applied them to predict the contents of four main components anthraquinones, anthranones, stilbenes and tannins, performance were 0.55, 0.73, 0.72 and 0.66, the correlation coefficient were 0.8831, 0.8690, 0.7415 and 0.7918, respectively. The aim of this study is to develop a NIRS method for determining chemical constituents in Chinese rhubarbs and to provide a comparative study of various training algorithms for MLPNNs for determining chemical constituents in rhubarbs.Infrared spectroscopy (IR), scanning electron microscope (SEM) and energy-dispersive X-ray analysis (EDX) were used to analyze nutrients in four different kinds of pine pollen powder samples. Based on the differenc... |
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