Identification Models for Chinese Herbal Medicines Using Near-Infrared Spectroscopy
Date Issued
2012
Date
2012
Author(s)
Wen Yang, Ci
Abstract
The inclusion of herbal medicines in modern medical treatment is steadily increasing. Many constituents of the herbal medicines are known and analyzed by scientists. The control and monitoring of the herbal medicine materials is a crucial work in the pharmaceutical factory. Rapid recognition of the plant species and varieties for herbal medicine is a big challenge. The identification by wet chemical methods is usually higher in cost and lower in efficiency. Compared with other modern inspection methods, near infrared (NIR) spectroscopy is an alternative, which is non-destructive, rapid, and easy to operate. There are six main research projects in this dissertation. Firstly is the previous backgrand study: the ability of using NIR spectroscopy in differentiating from 50 to 100 different herbal medicine raw materials was demonstrated. The examples included a variety of samples based on plant, fungi and animal derived materials. In addition, to simplify the identification, the author used hierarchical cluster analysis and other pattern recognition techniques, groupings of similar materials (based on NIR spectra, not priory groupings). The identification for the products in each grouping could then be definitively made using pattern recognition techniques that were customized for each distinct group of materials. Hierarchical clustering analysis (HCA), artificial neural network (ANN) and support vector machines (SVM) were applied to 244 herbal medicine raw materials classification problems by NIR. The SVM training resulted in models showing a method for the identification of herbal medicine raw materials. The results indicate when (HCA) distances were computed, 10 PCs were used to cover 95% of information. If threshold 1 is used, library will be divided into 16 clusters. The following different clusters can be used for the local ID methods developments. Clustering total samples into secondary groupings make identification more definitive. When using the feed forward network as a classifier, choose output neurons as many classes in the calibration dataset, each of the output neurons are set to react for only one specific material, if connected to the same hidden layers showed the better results on the same training times calibrations.
In the sencond study, 18 raw materials from Herbal Medicines industry were used to examine the FT-NIR performance. Regarding 71 samples in the calibration set, the identification accuracy was 100%. In the validation set of 34 samples, 33 samples were successfully discriminated, and the identification accuracy was 97%. As a result, the identification accuracy of 18 medicinal herbs with 105 samples was 99% using FT-NIR spectroscopy.
Furthermore, a robust identification model for herbal medicine was developed by combining near-infrared (NIR) spectroscopy and artificial neural network (ANN) to discriminate raw materials of herbal medicine, which are often similar in appearance and practically impossible to identify by visual inspection alone. The third part research was employed ANN to analyze the absorption spectra of herbal medicines and successfully built an identification model, which is able to identify 30 different herbal medicines. The best identification model can reach a correct identification rate (CIR) of 99.67% when applied to a training set of 600 samples, and 100% CIR when applied to a test set of 300 samples.
Moreover, because the storage and conveyance of the raw materials are always in dry powder forms before the materials are used in scientific pharmaceutical procedures, it is difficult to determine specific varieties of the herbal medicine constituents by visual observation of the raw materials at this step. Consequently, the development of a rapid and accurate inspection method and model for pharmaceutical factory applications is greatly needed. The fourth, a variety of herbal medicines according 48 raw materials with every material content 30 samples were measured using non-destructive near-infrared spectroscopy with soft independent modeling classification analogy (SIMCA) to build up the classification model. The adulterated samples could be eliminated by the analysis of the model, and identification rates were demonstrated in the range of 98 to 100%. The method could be applied not only to the pharmaceutical industry but also to the food industry. The food materials could be measured with the inspection model for effective identification and determination of adulteration.
The fifth study: because the traditional Chinese medicine (TCM) is the quintessence of Chinese culture with long history. It has, for many years, enriched the quality of people’s life. TCM is mainly obtained from nature. Unfortunately, some of the nature resources are no longer sustainable due to habitat destruction and over-exploitation. Many of the animals and plants that are used in TCM have become endangered. The objects of this part study was to establish a history of feeding and dietary husbandry of pangolin in captivity since 1877 to 2001, and discuss the methods of identify those endangered species which used in TCM by NIR and DNA analysis in the six part.
Finally, in addition on appendixs: there were 3 main studies in the appendix：1. Quantitative analysis of herbal materials, 2. Genseng and western ginseng study: species differentiation, geographical origins, quality assessment and 3. Fangij compare with the poisonous herbal material guangfangji by NIR.
Subjects
near infrared (NIR) spectroscopy
artificial neural network (ANN)
herbal medicine
soft independent modeling classification analogy (SIMCA)
classification
pangolin
Type
thesis
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