【作者】 王静；

【导师】 瞿海斌；

【作者基本信息】 浙江大学 ， 药物分析学， 2007， 硕士

【摘要】 我国的中药生产工艺落后,缺乏有效的在线监测技术,难以保证中药产品的质量。近红外光谱分析技术作为一种快速、无损的过程分析方法,已在众多领域得到应用。近年来,近红外光谱技术在中药制造过程的应用研究也逐渐增多,然而,其在中药喷雾制粒和醇沉过程分析中的应用研究鲜有报道。本文以养胃颗粒喷雾制粒和丹参注射液醇沉过程为具体研究对象,开展了近红外光谱分析技术在中药喷雾制粒和醇沉过程中的应用研究,主要内容包括:1.建立了同时测定无糖型养胃颗粒中没食子酸、芍药内酯苷、芍药苷和苯甲酰芍药苷含量的HPLC分析方法。该法分离效果好,方法简便、准确,适用于无糖型养胃颗粒的质量控制。2.将近红外光谱分析技术和移动块标准偏差法相结合,建立了一种可快速判断养胃颗粒喷雾制粒干燥终点的过程分析方法;同时建立了近红外光谱分析方法快速测定养胃颗粒中水分含量的方法。3.采用正交试验设计法,选择浓缩液密度、搅拌速度、乙醇加入速度、乙醇浓度、静置时间、冷藏温度和冷藏时间为考察因素,综合考虑各试验条件下醇沉液中丹参素含量、原儿茶醛含量和含固量,方差分析结果表明,乙醇浓度和丹参浓缩液密度对考察指标有较大影响。4.以正交试验结果为基础,以近红外光谱法为分析手段,建立了一种快速判别丹参注射液醇沉工艺稳定性的方法。以丹参注射液醇沉过程为例,采集各个醇沉条件下的近红外光谱图,建立近红外光谱的主成分分析模型,对生产工艺参数的变化进行判别。5.以偏最小二乘法(PLS)为建模方法,建立了可同时快速测定丹参注射液醇沉过程中丹参素、原儿茶醛和含固量的近红外定量分析方法。更多还原

【Abstract】 Traditional Chinese Medicine (TCM) is a historic industry in China, and its manufacture has been highly depended on experience. An effective on-line monitoring technique is absolutely necessary for the quality control of TCM. As a fast, non-destructive process analysis method, Near-Infrared Spectroscopy (NIRS) technique has been applied in many fields of industry, but a little in TCM manufacturing process. In this thesis, the application of NIRS technique to on-line quality analysis of TCM manufacturing process including Salvia miltiorrhiza ethanol precipitation and ’YangWei’ Granules spraying and drying granulation had been researched. The main content was summarized as follows:1. An HPLC method was established for determining gallic acid, albiflorin, paeoniflorin, and benzoylpaenoniflorin in Sugar-free ’YangWei’ Granules. The developed HPLC method provided an accurate and sensitive way to quantitative analysis of gallic acid, albiflorin, paeoniflorin and benzoylpaenoniflorin in Sugar-free ’YangWei’ Granules. It was suitable for the reference for NIR.2. On the basis of the result of HPLC analysis, taken spray granulation process of ’YanWei’ Granules as an example to develop a rapid method to monitor the endpoint in the process of flidized-bed spray granulation. Measure the content of water in products by the NIR model, and use Moving Block of Standard Deviation (MBSD) to judge the endpoint of the process.3. If the parameter was changed, the precipitation result would be changed subsequently. Taking ethanol precipitation process of Salvia miltiorrhiza as an example, the orthogonal experimental design was utilized to choose the most important ones from the following factors: the original cream concentration (ρ), first precipitation stirring velocity (V), first precipitation affiliate rate(x), first ethanol concentration (C), first static time (T), first cold temperature (W) and first frozen time (t). Evaluating with tanshinol content, protocatechuic aldehyde content, solid content and combined score, we found out thatρand C were the most influential factors. In order to verify the results, another experiment was carried out, whose results were consistent.4. On the basis of the results of orthogonal experiments, a NIR method was established to fast distinguish the stability of technics. Taking ethanol precipitation process of Salvia miltiorrhiza as an example, various spectrograms under different conditions were collected. Compared principal component analysis (PCA) models with different pretreatment methods and bands, finally chose the MSC pretreatment and 7070-9500 cm’1 band to establish PCA models. Analysed the spectrograms with PCA method under different ethanol content, batches of cream, cold temperature, sizes containers and numbers of precipitation. And it was found out that the different precipitation conditions could be distinguished using above PCA method.5. An online content analysis method was established to monitor Salvia miltiorrhiza ethanol precipitation process. A partial least squares (PLS) model was established using the selected wave bands, to measure the content of tanshinol, protocatechuic aldehyde and solid matters. It was proved that the model was competent for online analysis of Salvia miltiorrhiza ethanol precipitation process.更多还原