基于短波近红外光谱盐酸氟桂利嗪粉末药品质量比的估算方法

基于短波近红外(SW-NIR)漫反射光谱,提出一种非破坏定量预测盐酸氟桂利嗪粉末药品质量比的新方法.以高效液相色谱法(HPLC)分析值为参考值,将遗传算法(GA)和神经网络相结合,建立盐酸氟桂利嗪粉末药品的光谱特性与质量比间的定量分析模型.应用GA自动构建神经网络拓扑结构,选择最优的网络参数和光谱波段;讨论不同光谱预处理方法对神经网络预报能力的影响,并对测试集样品质量比进行预测.结果表明,一阶导数数据预处理方法为最优预处理建模方法,其测试集的均方根误差RMSE=0.198 7%,相关系数R=0.986 3.

Mass Ratio Determination of Compound Flunarizine Hydrochloride Powder Drug Baseed on Short-Wave Near-Infrared Diffuse Reflectance Spectral Data

A new method for the nondestructive quantitative prediction of flunarizine hydrochloride powder drug based on short wave near infrared (SW NIR) diffuse reflectance spectral data. With the analytical data of high performance liquid chromatography (HPLC) taken as the reference value, a quantitative analysis model for describing the relationship between the spectral characteristic and component was established via combining genetic algorithm (GA) withneural networks. The neural network topological framework was constructed automatically by means of GA. The optimal network parameters and spectral region wereselected. Moreover, the influences of different spectral preprocessing methods to the prediction ability of neural network were discussed. The mass ratio of the test sample was predicted. The results of the experiment show that the first derivative spectrum is the optimal pretreatment method of modeling, by which the root mean square errors (RMSE) for test set was 0.198 7%, and the correlation coefficients R for test set was 0.986 3.