Studies On Computational Methodologies Of Near Infrared Spectroscopy For TCM

The modernization and internationalization of Traditional Chinese Medicine (TCM) are blocked by its poor consistence and instability. One of the main reasons is the absences of fast and efficient analytical methods for quality control of TCM. Among the key technologies need to be investigated in pharmaceutical analysis field, one of main tasks is to develop fast analytical technologies and methodologies for TCM, establish on-line analytical methodologies for TCM process, realize the optimal control and evaluation of the quality of TCM in its pharmaceutical process and ensure consistence and equality of its quality. Near infrared spectroscopy (Near Infrared Spectroscopy, NIRS) is an advanced computer aided fast technique, and it could be a promising solution to solve the problem of fast measurement for TCM. Since the composition of TCM is quite complex, the traditional methods for modeling and pattern recognition sometimes could not get the ideal quantitative or qualitative results. So it is difficult to use NIR spectra practically in pharmaceutical industry of TCM. Therefore, it is necessary to carry on the studies on computational methodologies of NIRS, especially develop methods for non-linear modeling, feature extraction, chemical pattern information processing, fuzzy classification and etc., develop the new analytical technologies for pharmaceutical process of TCM and realize the quality control for the whole pharmaceutical process of TCM. Such studies have great scientific meaning and remarkable practical value in progress of TCM modernization.In this paper, some computational analytical methods have been developed according to the content and demand of quality control of TCM. These methods could be used to solve the application problems of using NIR technologies for TCM. The approaches improving the level of analysis and quality control of TCM are discussed, and could be used to develop new fast analytical methodologies of quality control for TCM. The main results obtained from the paper are listed as following:1. To distinguish quality of complex TCM, a fast analytical method using NIRS based on an adaptive fuzzy-neural classifier (FNN) is proposed. The experiment results on Colla CoriiA sini show the proposed method has strong learning and extrapolated abilities in distinguishing the quality of TCM with ambiguous boundary. Compared with method using traditional BP-ANN, its identification result is more accurate. It is showed that the method proposed is fast, convenient, non-destructive, and effective.2. In order to solve the problem of lacking fast method for evaluating quality of TCM injections, the methods using near infrared transmission spectroscopy based on a self-organizing mapping neural network (SOM) and FNN are developed respectively. Distinguishing different manufacturers of Shenmai injection is investigated as an example to test the performance of the methods. The methods developed could be used as new approaches to classify the complex TCM which has serious non-linear phenomena and could not be easily classified by traditional methods.3. Studies on the methodologies of determinating active components of TCM using NIR spectra analysis are systematically carried on. Methods using artificial neural network (ANN) and support vector machine (SVM) combined with different data preprocess methods are proposed to solve the non-linearity in the NIR reflectance spectra of Panax notoginseng root herb. These proposed methods effectively reduce the predictive error and are the suitable tools for non-linear modeling.4. Studies on applications of on-line NIRS technologies and computational analytical methodologies are carried on. Analytical models are successfully established to make a fast on-line measurement of Radix salviae miltiorrhizae's extraction in pilot-scale and industrial process respectively. The BP-ANN models are established to solve the non-linearity in the measured NIR spectra and improved the quantaitive analytical accuracy. A method using the absolute distance of standard deviation of the on-line measured spectra is developed to make fast estimation of process ending point during the optimization of TCM process. The results well inosculates the real industrial process. These methods developed could be applied inthe whole production process of TCM.In conclusion, the studies on computational analytical methodologies of NIRS for TCM help to improve the level of theoretically research and application of NIR technologies. These developed analytical methods could solve the problem of absence of fast methods of quality control for TCM. The results indicate that all the developed methods could be extent to fast quality identification and on-line measurement of TCM, having applicability and innovabality. It provides new approaches in strengthening the quality control in production process and improving the analytical level of TCM.