近红外光谱无损定量分析吡嗪酰胺片  

Non-destructive Quantitative Analysis of Pyrazinamide in Pyrazinamide Tablets with NIR

作　　者：孟庆繁[1] 候欣彤[2] 魏广英 逯家辉[1] 郭伟良[1] 滕利荣[1] 

机构地区：[1]吉林大学生命科学学院,长春13001 [2]吉林大学第二临床医学院 [3]长春市药品检验所

出　　处：《应用化学》2007年第10期1153-1156,共4页Chinese Journal of Applied Chemistry

基　　金：吉林省科技发展基金资助项目(20020503-2)

摘　　要：采用近红外光谱技术结合径向基神经网络(RBF)建立测定吡嗪酰胺片中吡嗪酰胺含量的定量分析模型。采用留一交互验证法选定模型的最有效的光谱预处理方法、网络的最适拓扑结构参数和扩展常数,所建立的分析模型用于预测预测集样品中的吡嗪酰胺含量,预测均方根误差(RMSEP)为0.00330。结果表明,方法方便快捷、无前期预处理和无污染,测量准确。Via near infrared （NIR） spectroscopy combined with radial basis function neural network （RBFNN）, a model for determining pyrazinamide（PZA） content in tablets was established. Leave-one-cross- validation method was used for selecting the most effective preprocessing method, the most suitable topological parameters and the best spread constant in the RBFNN. The results showed that Savitzky-Golay smoothing method was the most effective preprocessing method, and the most suitable number of input nodes and hidden nodes were 8 and 15 respectively, and the best spread constant was 2. 5. The optimum parameters were applied to establish the model for determining the pyrazinamide content in tablets with the root mean squares error cross-validation（RMSECV） of 0. 005 52. Using this model for determining the pyrazinamide content in the prediction set, the root mean squares error of prediction set （RMSEP） was 0. 003 30 and the average recovery was 100. 091%. NIR method and UV-spectroscopy method were used for determining the pyrazinamide content in 6 different batches of Pyrazinamide tablets respectively, and the relative error between the values obtained by these methods was less than 4. 179%. These results demonstrate that the NIR method is precise, convenient, and rapid, and involves no pretreatment and pollution, and may have extensive application in pharmaceutics quantitative analysis.

关 键 词：近红外光谱 径向基神经网络 吡嗪酰胺 定量分析 

分 类 号：O657[理学—分析化学] R927.2[理学—化学]