1. Department of Chemistry,Capital Normal University,Beijing 100037，China 2. School of Chinese Pharmacy,Beijing University of Chinese Medicine,Beijing 100102，China
Abstract：The first derivative and wavelet compression methods were used to eliminate the slope-background and reduce variables for the measured ultraviolet (UV) spectra of Chinese medicinal Qingkailing injections intermediate. Then，support vector machine (SVM) was used for building the classification model to discriminate qualified and unqualified samples. The effects of spectral preprocessing and model parameters were investigated. Under optimized conditions，correct classifications of 100%,95.4%,97.3%，and 100% were obtained for the four batches of the intermediate of Qingkailing injection samples，respectively. A percentage of 97.3% of the intermediate samples were correctly classified for all the four batches of mixture samples. Results showed that SVM technique can be a useful means for quality control of Chinese medicinal injections owing to its good accuracy and better generalization.
朱向荣1,李娜2,史新元2,乔延江2,张卓勇1*. 支持向量机与紫外光谱法用于鉴别清开灵注射液六混中间体[J]. 光谱学与光谱分析, 2008, 28(07): 1626-1629.
ZHU Xiang-rong1,LI Na2,SHI Xin-yuan2,QIAO Yan-jiang2,ZHANG Zhuo-yong1*. Study on the Chinese Medicinal Qingkailing Injections Intermediate by Support Vector Machines and Ultraviolet Spectrometry. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(07): 1626-1629.
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