Pharmaceutical Informatics Institute, Zhejiang University, Hangzhou 310058, China.
J Pharm Biomed Anal. 2010 Nov 2;53(3):350-8. doi: 10.1016/j.jpba.2010.04.011. Epub 2010 May 8.
A method for rapid quantitative analysis of four kinds of Tanreqing injection intermediates was developed based on Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares (PLS) algorithm. The NIR spectra of 120 samples were collected in transflective mode. The concentrations of chlorogenic acid, caffeic acid, luteoloside, baicalin, ursodesoxycholic acid (UDCA), and chenodeoxycholic acid (CDCA) were determined with the HPLC-DAD/ELSD as reference method. In the PLS calibration, the NIR spectra were pretreated with different methods and the number of PLS factors used in the model calibration was optimized by leave-one-out cross-validation. The performance of the final PLS models was evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and correlation coefficients (R). The R values in the prediction sets were all higher than 0.93, and the SEPs for the 6 compounds are 1.18, 6.02, 2.71, 155, 126, 30.0mg/l, respectively. The established models were used for the liquid preparation process analysis of Tanreqing injection in three batches, and a model updating method was proposed for the long-term usage of the established models. This work demonstrated that NIR spectroscopy is more rapid and convenient than the conventional methods to analyze the intermediates of Tanreqing injection, and the presented method is helpful to the implementation of process analytical technology (PAT) in pharmaceutical industry of Chinese Medicines Injections.
基于傅里叶变换近红外(FT-NIR)光谱和偏最小二乘法(PLS)算法,建立了一种快速定量分析四种注射用痰热清中间体的方法。采用漫反射模式采集了 120 个样品的近红外光谱。采用 HPLC-DAD/ELSD 作为参考方法,测定绿原酸、咖啡酸、芦丁、黄芩苷、熊去氧胆酸(UDCA)和鹅去氧胆酸(CDCA)的浓度。在 PLS 校准中,采用不同的方法对近红外光谱进行预处理,并通过留一法交叉验证优化模型校准中使用的 PLS 因子数。根据校准的均方根误差(RMSEC)、交叉验证的均方根误差(RMSECV)、预测的均方根误差(RMSEP)、偏差、预测标准差(SEP)和相关系数(R)评估最终 PLS 模型的性能。预测集的 R 值均高于 0.93,6 种化合物的 SEP 分别为 1.18、6.02、2.71、155、126、30.0mg/L。建立的模型用于三批痰热清注射液的液体制剂过程分析,并提出了一种用于长期使用建立模型的模型更新方法。这项工作表明,与传统方法相比,近红外光谱法更快速、更方便地分析痰热清注射液的中间体,所提出的方法有助于实现中药注射液的过程分析技术(PAT)。