作　　者： ; ; ; ; ;

机构地区： 首都师范大学化学系

出　　处： 《光谱学与光谱分析》 2008年第7期1626-1629,共4页

摘　　要： 采用一阶导数对紫外可见光谱数据进行预处理,消除光谱中的斜坡背景,用支持向量机(SVM)进行建模,分别对4个批次的中药清开灵注射液六混中间体以及其混和批次共147个样本进行了鉴别。对建模参数的影响作了系统的研究,在优化参数条件下,正确率分别可达100%,95.4%,97.3%,100%和97.3%。结果表明SVM鉴别准确率高,模型的泛化能力强。此方法也为中药注射液生产过程的质量控制提供了一条有效的途径。 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.

关 键 词： 支持向量机 紫外光谱 清开灵注射液 中间体

领　　域： [理学] [理学]