近红外漫反射光谱法快速判别注射用炎琥宁的冻干骨架结构

Rapid discrimination of the cage structures of lyophilized potassium sodium dehydroandroandrographolide succinate for injection with FT-NIRS and CP-ANN

目的:建立快速判别注射用炎琥宁冻干骨架结构的对向传播人工神经网络(counter-propagation artificial neural net-work,CP-ANN)近红外漫反射光谱法。方法:采用配有积分球附件的Antaris II傅里叶变换近红外光谱仪测定注射用炎琥宁的近红外漫反射光谱;用TQ Analyst 8.0进行光谱处理及数据预处理;用注射用炎琥宁校正样品的近红外漫反射光谱数据、以Matlab 6.5建立未损与已损冻干骨架结构的CP-ANN判别模型,并对模型进行交叉验证;用所建CP-ANN模型预测注射用炎琥宁验证样品冻干骨架结构的完整性。结果:所建近红外漫反射光谱CP-ANN判别模型预测注射用炎琥宁冻干骨架结构的准确率为100.0%,且具有良好的可视化功能。结论:所建方法判断客观,无损、无污染,简便快速,可望用于冻干注射用制剂的生产过程质量控制或临床使用质量控制。

OBJECTIVE To establish a method for rapid discrimination of the cage structures of lyophilized potassium sodium dehydroandroandrographolide succinate for injection with FT-NIRS and CP-ANN.METHODS The Fourier transform near infrared diffuse reflectance spectra were measured using Antaris II FT-NIR analyzer furnished with an integrating sphere attachment.The spectra processing and data pre-processing were done with the chemometric software TQ Analyst 8.0.The CPANN model for the rapid discrimination of intact and defective cage structures was built with Matlab6.5.RESULTS The prediction accuracy of the CP-ANN model was 100.0%.CONCLUSION The established model might be used to predict intact and defective cage structures of the lyophilized powder in the production and use.