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最小二乘支持向量机算法与紫外光谱法用于鉴别清开灵注射液四混中间体
引用本文:朱向荣,李娜,史新元,乔延江,张卓勇.最小二乘支持向量机算法与紫外光谱法用于鉴别清开灵注射液四混中间体[J].分析化学,2008,36(6):770-774.
作者姓名:朱向荣  李娜  史新元  乔延江  张卓勇
作者单位:1. 首都师范大学化学系,北京100037;北京中医药大学中药学院,北京100102
2. 北京中医药大学中药学院,北京,100102
3. 首都师范大学化学系,北京,100037
摘    要:采用一阶导数数据预处理,最小二乘支持向量机(LS-SVM)紫外可见光谱建模,对清开灵注射液四混中间体进行质量评价。以二次网格法和十折交叉验证法优化建模参数,预测集的总正确率和接受器工作特性曲线(ROC)下面积分别可达98.0%和0.983。结果表明,与经典的支持向量机相比,LSSVM鉴别准确率更高,模型的泛化能力更强。可用于清开灵注射液生产过程中质量控制,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。

关 键 词:清开灵注射液  中间体  紫外光谱法  最小二乘支持向量机

Study on Chinese Medicinal Qingkailing Injection Intermediate by Least Squares Support Vector Machines and Ultraviolet Spectrometry
ZHU Xiang-Rong,LI Na,SHI Xin-Yuan,QIAO Yan-Jiang,ZHANG Zhuo-Yong.Study on Chinese Medicinal Qingkailing Injection Intermediate by Least Squares Support Vector Machines and Ultraviolet Spectrometry[J].Chinese Journal of Analytical Chemistry,2008,36(6):770-774.
Authors:ZHU Xiang-Rong  LI Na  SHI Xin-Yuan  QIAO Yan-Jiang  ZHANG Zhuo-Yong
Abstract:The first derivative spectra with selected wavelengths were used to eliminate the slope-background and reduce variables for the measured ultraviolet(UV)spectra of Chinese medicinal Qingkailing injection intermediates.Then,least squares support vector machine(LS-SVM)was used for building the classification model to discriminate 196 injections intermediate samples(116 qualified and 80 unqualified samples).The modeling parameters were investigated using two-grid searching and ten-fold cross-validation methods.Under the optimized conditions,the predictive ability of the testing set and the area under receiver operation characteristic(ROC)curves(AUR)reach 98.0% and 0.983,respectively.Comparing with the conventional support vector machine(SVM),LS-SVM was found better accuracy and generalization.Results showed that LS-SVM technique can be a useful means for quality control of Chinese medicinal injection in the production process and other Chinese medicines.
Keywords:Qingkailing injection  intermediate  ultraviolet spectrometry  least squares support vector machine
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