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Glycosaminoglycan is a kind of Biological Macromolecule. It’s bioactivity is tightly related to the physicochemical property, and the majority of it is non-toxic. With the development of biodegradable polymer, natural medicine and structural molecular biology, the glycosaminoglycan has been made great progress in the area of Pharmacology, Molecular biology and clinical application. In the last few years, Hyaluronic acid(HA), heparin(Hep), chondroitin sulfate(CS) are glycosaminoglycans, and they are ideal drugs in clinical application. As the anticoagulant and antithrombotic drugs heparin has been used in blood dialysis and in the blood circulation in vitro to prevent blood coagulation and thrombus formation over 90 years of clinical application. Hyaluronic acid increases the lubrication between the joints, damps the effect of strenuous exercise, and avoids the joints harm. Chondroitin sulfate has a special role in the pharmacological immunosuppression, it can prevent atherosclerosis, and has good results in clinical treatment for arthritis, rheumatic pain, neuralgia and low back pain.The chemical structure of HA, Hep and CS are similar, and they are all white powder, so it is difficult to identify them. Because of the price and preparation in medicine, it is narmal to falsify the glycosaminoglycans. In order to completely eradicate the fake and inferior drugs to get better economic efficiency, it’s quite important to identify impurity and different glycosaminoglycans in raw materal and preparation. However, the routine analysis method is destructive, and it needs pretreatment and organic reagent and it nees long time most of all. Therefore, it’s quite necessary to use an undestructive, rapid and non-pollution method.Near infrared spectroscopy techonology (NIRS) is a green method without specail pretreatment, pollution and destructive. It can be used in inline and remote analysis, and it has been widely used in chemical, agriculture, food and pharmaceutical.In the experiment we collected the spectrums of HA, Hep, and CS with near infrared spectroscopy instrument, then we used nonsupervised and supervised classification methods to identify the three differernt types of glycosaminoglycans. We used hierarchical clustering analysis (HCA) and K-means method to separate the three types, and we got good results. In the supervised classification we used soft independent modeling of class analogy (SIMCA) to identify three different types of glycosaminoglycans with different pretreatment including smoothing with 5 points, SNV, and first derivative with smoothing of 5 points. Meanwhile we investigated the effect of different wave range and different sample number to the mathematical model. It is proved that the mathematical model with the preteatment of smoothing with 5 points,25 samples and whole wave range was good enough to separate the three glycosaminoglycans rapidly and accurately. The conclution of the thesis lay foundations to identify drugs with similar aspect.
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