近红外光谱法探索在线监测注射用五水头孢唑林钠干燥过程中的水分含量

发布时间：2018-05-09 21:36

本文选题：近红外光谱法 + 过程分析技术　；参考：《沈阳药科大学学报》2017年07期

【摘要】：目的建立在线监测注射用头孢唑林钠干燥过程中的水分含量测定方法。方法水分定量模型建模样品采以不同批次、不同干燥时间段五水头孢唑林钠原料为研究对象,结合干燥、引湿实验扩充样品水分范围,用积分球测定近红外漫反射光谱,并采用聚类分析挑选出建模光谱,光谱预处理方法为一阶导数+MSC法,谱段为7502~6499.2、5500.2~3 999.8 cm~(-1),回归方法偏最小二乘法。结果水分定量模型建模样品共计85个,水分含量质量分数为12.15%~16.41%,相关系数(coefficient of determination,R~2)为96.30,交叉验证均方差(root mean square error of cross validation,RMSECV)为0.181。外部验证样品52个,水分含量质量分数为13.28%~16.17%,外部验证均方差(root mean square error of predication,RMSEP)为0.149。模型专属性强,准确度、线性和精密度符合要求。结论建立的运用近红外监测注射用头孢唑林钠干燥过程的水分含量方法是可行的,为实现近红外在线干燥提供了实验基础。
[Abstract]:Objective to establish an on-line monitoring method for the determination of water content of cefazolin sodium for injection during drying. Methods the water quantitative model samples were collected from different batches and different drying periods of cefazolin sodium pentahydrate. The moisture range of the samples was expanded by drying and wetting experiments, and the near infrared diffuse reflectance spectra were measured with integral sphere. The model spectrum was selected by cluster analysis. The first derivative MSC method was used to pretreat the spectrum, and the spectral segment was 7502O6499.2X 5500.2U 3 999.8 cm ~ (-1), and the regression method was partial least square method. Results A total of 85 water quantitative model samples were established, the water content mass fraction was 12.15% 16.41, the correlation coefficient of determination R2 was 96.30, and the cross validation RMSECV was 0.181. For 52 external validation samples, the mass fraction of water content was 13.28% and 16.17%, and the root mean square error of prediction RMS EPs was 0.149. Model specificity, accuracy, linearity and precision meet the requirements. Conclusion the established method for monitoring the moisture content in the drying process of cefazolin sodium for injection is feasible and provides an experimental basis for the on-line drying of cefazolin for injection.
【作者单位】：沈阳药科大学药学院;深圳华润九新药业有限公司;中国食品药品检定研究院;
【基金】：深圳市战略新兴产业发展专项资金资助项目(编号CXZZ20130508105318972)
【分类号】：O657.33;R927.2

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