近红外光谱分析技术在白芍中药配方颗粒制备过程中的应用研究
|
摘要
目前,有关中药配方颗粒生产过程的质量控制研究一般是针对少数关键生产环节中间体及工艺终点的产品进行质量分析,而关于其生产过程在线质量监控的研究鲜有报道。中药配方颗粒生产过程为典型的批次生产过程,其生产过程由一系列单元操作如提取、浓缩、干燥等组成,每一项单元操作工艺参数的变化都会影响到最终产品的质量。由于缺乏有效的在线质量分析方法,生产过程往往只能依赖经验进行操作,根据经验决定过程是否完成,从而导致产品难以达到国际上对药品质量稳定均一的要求,严重制约了中药配方颗粒工业化、国际化的发展。
近红外光谱技术(Near-Infrared Spectroscopy,NIRS)是目前最常用的过程分析技术(Process Analytical Technology,PAT)之一,它是20世纪80年代后期迅速发展起来的一种新型分析检测方法,它不需对样品进行复杂的预处理就可直接进行测试,测试过程通常在几分钟之内就能完成,而且整个测试过程不需要使用和消耗任何化学试剂,是一种“绿色、环保”型分析检测方法,已在农产品、石油化工、生命科学及药物分析等多个领域得到广泛应用。
由于中药配方颗粒为单味药材提取物,与复方中药相比,其物质组成简单,基础研究较深入,是NIRS技术在中药生产质量控制示范研究的良好载体。将近红外光谱分析技术应用于中药配方颗粒制备过程质量控制,建立过程中关键操作单元如提取、浓缩、干燥等近红外(NIR)在线监测方法,及时从实际制备过程中得到物料性质信息,了解关键过程参数与最终产品质量之间的关系,对制备过程工艺参数进行即时调控,改变目前根据经验决定过程是否完成的质控手段,从而达到控制和优化药物制备过程的目的,使得不同批次的产品达到质量稳定均一的要求,促进中药配方颗粒进一步的发展。
本文以白芍配方颗粒制备过程为具体研究对象,开展了近红外光谱分析技术在白芍配方颗粒制备过程重要生产环节中的应用研究及Box- Behnken效应面法在优化中药提取工艺的应用研究,取得了较满意的结果。
1.中药药材的质量控制在整个中药配方颗粒生产过程中占有重要的地位,不同产地及不同批次的药材质量的稳定与否直接影响到后续工序的操作和产品的最终质量。本文所选用的白芍药材均来自全国各不同产地,具有一定的代表性,所有样品均在同一条件下进行粉碎,并过80目药典筛。运用偏最小二乘法(PLS)建立白芍药材粉末近红外光谱(NIR)与其芍药苷、芍药内酯苷和水分含量测定值之间的多元校正模型,校正模型相关系数(R2)分别为0.938、0.943、0.976。对验证集样品进行预测,预测结果表明,该法可以较准确地预测样品中的芍药内酯苷和水分的含量,预测平均相对偏差分别为0.23%、3.82%,而部分样本芍药苷的含量偏低,在近红外光谱法的检测低限,测定效果不甚理想。近红外光谱法具有分析速度快、预测结果准确、不破坏样品和不污染环境等优点,而且不需要对样品进行复杂繁琐的前处理,适合对组成复杂的中药进行快速分析,可用于白芍配方颗粒原料药材的质量监控。
2.Box-Behnken效应面法是采用多元二次方程拟合因素和效应值之间的函数关系,并通过对回归方程的分析寻求最优工艺参数,解决多变量问题的一种统计方法。本文以白芍总苷提取量为量化指标,在单因素实验的基础上,探索了用Box-Behnken效应面法确定白芍总苷提取的最优工艺参数的可行性。取得了比较满意的结果,并最终确定其最佳提取工艺参数为：浸泡时间为98.4min,加水量为12倍,提取时间为69.8min。在此条件下,白芍总苷的提取量为2.39%,与理论值仅相差0.014%,从而说明了Box- Behnken效应面法在中药提取工艺应用中的可行性。
3.中药提取过程是中药配方颗粒生产过程中的关键环节,缺乏主要成分含量在线监控是制约产品质量稳定性的瓶颈。本文将白芍一次提取和二次提取过程作为研究对象,运用近红外光谱法结合PLS法建立白芍提取液中芍药内酯苷及芍药苷含量的定量校正模型,结果表明所建芍药内酯苷和芍药苷含量NIR定量校正模型的预测能力较好,样品中芍药内酯苷和芍药苷含量预测值与参考值之间的偏差较小,预测值的平均相对偏差分别为1.51%,-1.87%,预测平均回收率分别为100.48%,98.13%,说明所建立的近红外光谱定量校正模型可以用于实时监控白芍提取过程中提取液所含芍药内酯苷、芍药苷含量的变化,并能对提取过程终点进行判断。
4.浓缩是中药配方颗粒生产过程中的重要操作单元,不同批次浸膏质量的稳定与否直接影响到后续工序的操作和产品的最终质量。本文以白芍水提液浓缩过程为例,将近红外光谱技术用于白芍水提液浓缩过程在线分析。利用PLS2法建立的近红外光谱校正模型能够快速预测浓缩液中芍药内酯苷和芍药苷的含量,及时获得浓缩过程药液有效成分的信息,并对浓缩终点作出判断。模型预测值与参考值之间具有较好的相关性,预测相对平均偏差分别为-1.16%,0.94%,表明利用近红外光谱技术能实时反映浓缩过程中料液的状态,提高中药浸膏质量的稳定性。
5.喷雾干燥法具有干燥速度快、受热时间短、生产工序少、产品流动性好、质地均匀、溶解性好等特点。近年来,我国中药产业发展迅速,在众多的中药制药企业中,喷雾干燥技术应用极其广泛。本文在单因素实验的基础上,以产物水分含量为指标,采用正交试验法研究喷雾干燥时料液相对密度、进/出风温度、进料速度对白芍提取物喷雾干燥的影响,并确定了最佳喷雾干燥工艺参数为：进/出风温度为190/90℃,料液相对密度1.08(60℃),进料速度为7mL/min。
6.本文将白芍提取液喷雾干燥过程作为研究对象,综合考察提取操作参数对产物水分含量及芍药内酯苷、芍药苷含量的影响,运用NIR光谱法结合PLS法和PCR法建立白芍喷干粉中水分、芍药内酯苷以及芍药苷含量的定量校正模型,结果表明上述所建水分、芍药内酯苷和芍药苷含量NIR定量校正模型的预测能力较好,样品中水分、芍药内酯苷和芍药苷含量预测值与参考值之间的偏差较小,预测值的平均相对偏差分别为-1.38%,0.30%、0.12%,预测平均回收率分别为98.62%,100.30%、100.12%,说明近红外光谱技术适用于喷雾干燥过程的在线质量控制。
以上研究结果表明,近红外光谱分析技术可用于白芍配方颗粒制备过程主要环节的过程质量控制。如能结合本文研究成果,并根据中药配方颗粒的生产特点,建立一套完善的近红外在线检测体系,实时监控中药配方颗粒生产加工过程,通过分析原料药及其制品加工过程中物理性质及化学成分指标变化来控制中药配方颗粒的质量状况,并且建立完善的近红外检测中药配方颗粒的模型库并不断扩充模型,这样就可以有效的控制中药配方颗粒的质量,确保产品的质量稳定和可重复性。相信近红外光谱技术在中药配方颗粒生产领域将具有非常广阔的应用和发展前景。
At present, the quality control of the Traditional Chinese Medicine (TCM) formula granule production process generally involves only the content ana-lysis at the end of the process, and on-line quality control of the process has been reported rarely. Production process of TCM formula granule is typical batch process, consist of a series units such as extraction, concentration, drying, etc., a change in parameter of each unit will affect the final product quality. Lack of effective on-line quality analysis method, production oper-ations often rely on experience, decision process is complete based on exp-erience, leading to product hard to reach for international drug quality stablility requirements, which seriously restricted the TCM formula granule industrialization and internationalization development.
Near infrared spectroscopy (NIRS) is the most commonly used technology for process analysis, which is a new type of detection method, developing rapidly since the late 1980s, the samples does not need complex pretreatment can be directly tested, the testing process is usually done in a few minutes, and the whole testing process doesn't need to use any chemical reagent, it is a kind of environmental protection analysis and detection methods, it has been widely used in agricultural, petrochemical, life sciences and pharmaceutical analysis and other fields.
Near infrared spectroscopy technology applied in TCM formula granule production process, establishing near infrared (NIR) on-line monitoring method in the production process key operation units such as extraction, concentra-tion, drying, etc., getting the material properties information from the actual production process timely, to understand the relationship between the key process parameters and the quality of the final product, thus to achieve production process parameter real-time control and optimization, making different batches of product achieve quality stablility requirements, and promoting the further development of TCM formula granule.
In this paper, the application of NIRS technique to on-line monitor the production process of peony formula granule had been researched, and achieved satisfactory results.
1. Quality control of herbal medicine in the whole production process of TCM formula granule plays an important role, the quality of medicinal materials which come from different areas and different batches exist certain differen-ces, which directly affects the subsequent operation process and the final product quality. Radix Paeoniae Alba selected in this paper come from different areas across the country, has certain representativeness, all samples were crushed under the same conditions, and pass 80 mesh pharmacopoeia sieve. Multivariate calibration models based on partial least squares (PLS) algorithm were developed to correlate the spectra and the corresponding values determ-ined by the reference method. The corelation coefficients (R2)of the calibra-tion models were 0.938、0.943 and 0.976, and the prediction average relative deviation for paeoniflori、albiflorin and moisture content were 6.46%、0.23% and 3.82%, respectively. The NIRS determination method was rapidly, accuracy, nondestructive and non-pollution. It can dispose the samples without compli-cated pretreatment. It is qualified to analyze TCM whose components are complex rapidly. NIRS can be used to control the quality of herbal material of paeony formula granule.
2. Box-Behnken response surface method is a statistical method that use multiple quadratic equation to fit the relations between factors and the responses, through the analysis of the regression equation to find the optimum process parameters,to solve the multivariate problems. In this article, base on the results of single factor experiment, Box-Behnken experimental design combining with response surface methodology(RSM) was employed, with the soa-king time (X1), the water addition (X2), the extraction time (X3) as the inde-pendent variables. The response variable was the amount of Total Glucoside of Paeony. The optimization Processing Parameters was as follows:X1，X2and X3 levels of 98.4min,12 times and 69.8min, respectively. The observed responses are in close agreement with the predicted values of the mathematic models, and the Box-Behnken response surface method is suitable for optimizing the extraction of TCM.
3. The extraction process of TCM is a key step in the production process, due to lack of on-line monitoring method, it is difficult to ensure the sta-bility of product quality. In this paper, the extraction process as the research object, multivariate calibration models based on partial least squares (PLS) algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of albiflorin、paeoniflorin were 1.51%、-1.87%, respectively, the prediction average recovery were 100.48%,98.13%, respectively. That the established quantitative NIR calibration model can be used in the extraction process to on-line monitor the content changge of albiflorin and paeoniflorin.
4. Extraction is an important step in the TCM production process, the quality of different batches extract exist differences, which directly affects the subsequent operation process and the final product quality. In this paper, the concentration process as the research object, multivariate calibration models based on PLS2 algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of albiflorin、paeoniflorin were-1.16%、0.94%, respectively. That the use of near infrared spectroscopy reflect the enrichment process in real-time liquid state, to improve the quality of traditional Chinese medicine extract stability. Through using the NIRS technology, the situation of medicine liquid in the extraction process can be reflected, Therefore, the stability of the extract quality to obtain improved.
5. The spray drying method has the characteristics such as fast-drying, heating time is short, less production processes, good mobility, homogeneous texture, good solubility,etc. In recent years, TCM industry has developed rapidly, the spray drying technology is widely used in many pharmaceutical enterprise. In this paper, the optimum technological conditions of the spray drying process of paeony extract were researched. According to the single factor experiment and the orthogonal test of 3 factors and 3 levels was ado-pted, and the optimum combination was obtained. the result showed the optimum conditions of spray drying process were as followed:inlet air temperature and outlet air temperature were 190℃and 90℃, the relative density of the extract was 1.08 (60℃), inlet flow rate was 7mL/min.
6.In this paper,NIRS technology applied in the spray drying process of paeony extract were researched, multivariate calibration models based on PLS and PCR algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of moisture content、albiflorin、paeoniflorin were-1.38%、0.30%,0.12%, respectively, the prediction average recovery were 98.62%, 100.30%,100.12%, respectively. That the established quantitative NIR calibration model can be used in the spray drying process.
The research results show that NIRS technology can be used to on-line monitor the major parts of production process of Paeony formula granule. If this research achievement and the characteristics of TCM formula granule production process are combined, set up a complete NIRS on-line detection system to monitor the production process, and improve the detection model of NIRS continually, This can control the quality of TCM formula granule effectively and ensure the stablility and repeatability of product quality.
近红外光谱技术(Near-Infrared Spectroscopy,NIRS)是目前最常用的过程分析技术(Process Analytical Technology,PAT)之一,它是20世纪80年代后期迅速发展起来的一种新型分析检测方法,它不需对样品进行复杂的预处理就可直接进行测试,测试过程通常在几分钟之内就能完成,而且整个测试过程不需要使用和消耗任何化学试剂,是一种“绿色、环保”型分析检测方法,已在农产品、石油化工、生命科学及药物分析等多个领域得到广泛应用。
由于中药配方颗粒为单味药材提取物,与复方中药相比,其物质组成简单,基础研究较深入,是NIRS技术在中药生产质量控制示范研究的良好载体。将近红外光谱分析技术应用于中药配方颗粒制备过程质量控制,建立过程中关键操作单元如提取、浓缩、干燥等近红外(NIR)在线监测方法,及时从实际制备过程中得到物料性质信息,了解关键过程参数与最终产品质量之间的关系,对制备过程工艺参数进行即时调控,改变目前根据经验决定过程是否完成的质控手段,从而达到控制和优化药物制备过程的目的,使得不同批次的产品达到质量稳定均一的要求,促进中药配方颗粒进一步的发展。
本文以白芍配方颗粒制备过程为具体研究对象,开展了近红外光谱分析技术在白芍配方颗粒制备过程重要生产环节中的应用研究及Box- Behnken效应面法在优化中药提取工艺的应用研究,取得了较满意的结果。
1.中药药材的质量控制在整个中药配方颗粒生产过程中占有重要的地位,不同产地及不同批次的药材质量的稳定与否直接影响到后续工序的操作和产品的最终质量。本文所选用的白芍药材均来自全国各不同产地,具有一定的代表性,所有样品均在同一条件下进行粉碎,并过80目药典筛。运用偏最小二乘法(PLS)建立白芍药材粉末近红外光谱(NIR)与其芍药苷、芍药内酯苷和水分含量测定值之间的多元校正模型,校正模型相关系数(R2)分别为0.938、0.943、0.976。对验证集样品进行预测,预测结果表明,该法可以较准确地预测样品中的芍药内酯苷和水分的含量,预测平均相对偏差分别为0.23%、3.82%,而部分样本芍药苷的含量偏低,在近红外光谱法的检测低限,测定效果不甚理想。近红外光谱法具有分析速度快、预测结果准确、不破坏样品和不污染环境等优点,而且不需要对样品进行复杂繁琐的前处理,适合对组成复杂的中药进行快速分析,可用于白芍配方颗粒原料药材的质量监控。
2.Box-Behnken效应面法是采用多元二次方程拟合因素和效应值之间的函数关系,并通过对回归方程的分析寻求最优工艺参数,解决多变量问题的一种统计方法。本文以白芍总苷提取量为量化指标,在单因素实验的基础上,探索了用Box-Behnken效应面法确定白芍总苷提取的最优工艺参数的可行性。取得了比较满意的结果,并最终确定其最佳提取工艺参数为：浸泡时间为98.4min,加水量为12倍,提取时间为69.8min。在此条件下,白芍总苷的提取量为2.39%,与理论值仅相差0.014%,从而说明了Box- Behnken效应面法在中药提取工艺应用中的可行性。
3.中药提取过程是中药配方颗粒生产过程中的关键环节,缺乏主要成分含量在线监控是制约产品质量稳定性的瓶颈。本文将白芍一次提取和二次提取过程作为研究对象,运用近红外光谱法结合PLS法建立白芍提取液中芍药内酯苷及芍药苷含量的定量校正模型,结果表明所建芍药内酯苷和芍药苷含量NIR定量校正模型的预测能力较好,样品中芍药内酯苷和芍药苷含量预测值与参考值之间的偏差较小,预测值的平均相对偏差分别为1.51%,-1.87%,预测平均回收率分别为100.48%,98.13%,说明所建立的近红外光谱定量校正模型可以用于实时监控白芍提取过程中提取液所含芍药内酯苷、芍药苷含量的变化,并能对提取过程终点进行判断。
4.浓缩是中药配方颗粒生产过程中的重要操作单元,不同批次浸膏质量的稳定与否直接影响到后续工序的操作和产品的最终质量。本文以白芍水提液浓缩过程为例,将近红外光谱技术用于白芍水提液浓缩过程在线分析。利用PLS2法建立的近红外光谱校正模型能够快速预测浓缩液中芍药内酯苷和芍药苷的含量,及时获得浓缩过程药液有效成分的信息,并对浓缩终点作出判断。模型预测值与参考值之间具有较好的相关性,预测相对平均偏差分别为-1.16%,0.94%,表明利用近红外光谱技术能实时反映浓缩过程中料液的状态,提高中药浸膏质量的稳定性。
5.喷雾干燥法具有干燥速度快、受热时间短、生产工序少、产品流动性好、质地均匀、溶解性好等特点。近年来,我国中药产业发展迅速,在众多的中药制药企业中,喷雾干燥技术应用极其广泛。本文在单因素实验的基础上,以产物水分含量为指标,采用正交试验法研究喷雾干燥时料液相对密度、进/出风温度、进料速度对白芍提取物喷雾干燥的影响,并确定了最佳喷雾干燥工艺参数为：进/出风温度为190/90℃,料液相对密度1.08(60℃),进料速度为7mL/min。
6.本文将白芍提取液喷雾干燥过程作为研究对象,综合考察提取操作参数对产物水分含量及芍药内酯苷、芍药苷含量的影响,运用NIR光谱法结合PLS法和PCR法建立白芍喷干粉中水分、芍药内酯苷以及芍药苷含量的定量校正模型,结果表明上述所建水分、芍药内酯苷和芍药苷含量NIR定量校正模型的预测能力较好,样品中水分、芍药内酯苷和芍药苷含量预测值与参考值之间的偏差较小,预测值的平均相对偏差分别为-1.38%,0.30%、0.12%,预测平均回收率分别为98.62%,100.30%、100.12%,说明近红外光谱技术适用于喷雾干燥过程的在线质量控制。
以上研究结果表明,近红外光谱分析技术可用于白芍配方颗粒制备过程主要环节的过程质量控制。如能结合本文研究成果,并根据中药配方颗粒的生产特点,建立一套完善的近红外在线检测体系,实时监控中药配方颗粒生产加工过程,通过分析原料药及其制品加工过程中物理性质及化学成分指标变化来控制中药配方颗粒的质量状况,并且建立完善的近红外检测中药配方颗粒的模型库并不断扩充模型,这样就可以有效的控制中药配方颗粒的质量,确保产品的质量稳定和可重复性。相信近红外光谱技术在中药配方颗粒生产领域将具有非常广阔的应用和发展前景。
At present, the quality control of the Traditional Chinese Medicine (TCM) formula granule production process generally involves only the content ana-lysis at the end of the process, and on-line quality control of the process has been reported rarely. Production process of TCM formula granule is typical batch process, consist of a series units such as extraction, concentration, drying, etc., a change in parameter of each unit will affect the final product quality. Lack of effective on-line quality analysis method, production oper-ations often rely on experience, decision process is complete based on exp-erience, leading to product hard to reach for international drug quality stablility requirements, which seriously restricted the TCM formula granule industrialization and internationalization development.
Near infrared spectroscopy (NIRS) is the most commonly used technology for process analysis, which is a new type of detection method, developing rapidly since the late 1980s, the samples does not need complex pretreatment can be directly tested, the testing process is usually done in a few minutes, and the whole testing process doesn't need to use any chemical reagent, it is a kind of environmental protection analysis and detection methods, it has been widely used in agricultural, petrochemical, life sciences and pharmaceutical analysis and other fields.
Near infrared spectroscopy technology applied in TCM formula granule production process, establishing near infrared (NIR) on-line monitoring method in the production process key operation units such as extraction, concentra-tion, drying, etc., getting the material properties information from the actual production process timely, to understand the relationship between the key process parameters and the quality of the final product, thus to achieve production process parameter real-time control and optimization, making different batches of product achieve quality stablility requirements, and promoting the further development of TCM formula granule.
In this paper, the application of NIRS technique to on-line monitor the production process of peony formula granule had been researched, and achieved satisfactory results.
1. Quality control of herbal medicine in the whole production process of TCM formula granule plays an important role, the quality of medicinal materials which come from different areas and different batches exist certain differen-ces, which directly affects the subsequent operation process and the final product quality. Radix Paeoniae Alba selected in this paper come from different areas across the country, has certain representativeness, all samples were crushed under the same conditions, and pass 80 mesh pharmacopoeia sieve. Multivariate calibration models based on partial least squares (PLS) algorithm were developed to correlate the spectra and the corresponding values determ-ined by the reference method. The corelation coefficients (R2)of the calibra-tion models were 0.938、0.943 and 0.976, and the prediction average relative deviation for paeoniflori、albiflorin and moisture content were 6.46%、0.23% and 3.82%, respectively. The NIRS determination method was rapidly, accuracy, nondestructive and non-pollution. It can dispose the samples without compli-cated pretreatment. It is qualified to analyze TCM whose components are complex rapidly. NIRS can be used to control the quality of herbal material of paeony formula granule.
2. Box-Behnken response surface method is a statistical method that use multiple quadratic equation to fit the relations between factors and the responses, through the analysis of the regression equation to find the optimum process parameters,to solve the multivariate problems. In this article, base on the results of single factor experiment, Box-Behnken experimental design combining with response surface methodology(RSM) was employed, with the soa-king time (X1), the water addition (X2), the extraction time (X3) as the inde-pendent variables. The response variable was the amount of Total Glucoside of Paeony. The optimization Processing Parameters was as follows:X1，X2and X3 levels of 98.4min,12 times and 69.8min, respectively. The observed responses are in close agreement with the predicted values of the mathematic models, and the Box-Behnken response surface method is suitable for optimizing the extraction of TCM.
3. The extraction process of TCM is a key step in the production process, due to lack of on-line monitoring method, it is difficult to ensure the sta-bility of product quality. In this paper, the extraction process as the research object, multivariate calibration models based on partial least squares (PLS) algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of albiflorin、paeoniflorin were 1.51%、-1.87%, respectively, the prediction average recovery were 100.48%,98.13%, respectively. That the established quantitative NIR calibration model can be used in the extraction process to on-line monitor the content changge of albiflorin and paeoniflorin.
4. Extraction is an important step in the TCM production process, the quality of different batches extract exist differences, which directly affects the subsequent operation process and the final product quality. In this paper, the concentration process as the research object, multivariate calibration models based on PLS2 algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of albiflorin、paeoniflorin were-1.16%、0.94%, respectively. That the use of near infrared spectroscopy reflect the enrichment process in real-time liquid state, to improve the quality of traditional Chinese medicine extract stability. Through using the NIRS technology, the situation of medicine liquid in the extraction process can be reflected, Therefore, the stability of the extract quality to obtain improved.
5. The spray drying method has the characteristics such as fast-drying, heating time is short, less production processes, good mobility, homogeneous texture, good solubility,etc. In recent years, TCM industry has developed rapidly, the spray drying technology is widely used in many pharmaceutical enterprise. In this paper, the optimum technological conditions of the spray drying process of paeony extract were researched. According to the single factor experiment and the orthogonal test of 3 factors and 3 levels was ado-pted, and the optimum combination was obtained. the result showed the optimum conditions of spray drying process were as followed:inlet air temperature and outlet air temperature were 190℃and 90℃, the relative density of the extract was 1.08 (60℃), inlet flow rate was 7mL/min.
6.In this paper,NIRS technology applied in the spray drying process of paeony extract were researched, multivariate calibration models based on PLS and PCR algorithm were developed to correlate the spectra and the corresponding values determined by the reference method. The prediction average relative deviation for the content of moisture content、albiflorin、paeoniflorin were-1.38%、0.30%,0.12%, respectively, the prediction average recovery were 98.62%, 100.30%,100.12%, respectively. That the established quantitative NIR calibration model can be used in the spray drying process.
The research results show that NIRS technology can be used to on-line monitor the major parts of production process of Paeony formula granule. If this research achievement and the characteristics of TCM formula granule production process are combined, set up a complete NIRS on-line detection system to monitor the production process, and improve the detection model of NIRS continually, This can control the quality of TCM formula granule effectively and ensure the stablility and repeatability of product quality.
引文
[1]曹姬姬.过程分析技术在制药工业的应用[J].医药工程设计,2006；27(5)：28-29.
[2]Blanco M, Villmroya I. Trends in Analytical Chemistry[J].2002; 21(4): 240-250.
[3]陆婉珍,褚小立.近红外光谱(NIR)和过程分析技术(PAT)[J].现代科学仪器,2007；(4)：13-17.
[4]FDA, "Guidance for Industry:PAT-A Framework for Innovative Pharmac-eutical Development, Manufacturing and Quality Assurance", September 2004.
[5]杨南林.基于近红外光谱的中药过程分析方法研究[D].杭州：浙江大学,2003,3-6.
[6]Callis J. B, lllman R. L., Kowalski B. R., Process analytical chemistry, Anal Chem,1987; 59(9):624A.
[7]沈虹,邓玉霞,吴诚.过程分析化学的提出与进展[J].理化检验-化学分册,1998；34(10)：473-477.
[8]李华,高鸿.过程分析化学的新进展[J].分析化学,2001；29(4)：473-477.
[9]陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M],北京：中国石化出版社,2007,2：1-11.
[10]Siesler H W, Ozaki Y, Kawata S. Near infrared spectroscopy:Principle instruments and application[M], Weinheim:Wiley-VCH,2002,1:86-88.
[11]Huck C W. Advances of near-infrared spectroscopy in phytochemistry [J].Near Infrared Spec.,2002; 10:491-496.
[12]Ingle J D Jr, Crouch S R. Spectrochemical Analysis[M], New York:Prentice Hall Englewood Cliffs,1988,1:113-114.
[13]Osborne B G, Fearn T. Near Infrared Spectroscopy in Food Analysis[M], New York:John Woley and Sons Inc.,1986,1:135-137.
[14]宋岩.近红外光谱技术在药物检测中的应用研究[D].长春：吉林大学,2009,2-3.
[15]朱韧.近红外在线光谱分析仪的研制[D].杭州：浙江大学,2008,6-7.
[16]田地,金钦汉,近红外光谱仪器[J].分析仪器,2001；(3)：39-42.
[17]徐广通,袁洪福,陆婉珍,近红外光谱仪器概况与进展[J].现代科学仪器,1997；(3)：9-11.
[18]孙宏伟,近红外光谱分析仪器的应用与发展[J].仪器评价,2002；(5)：26-28.
[19]王彬.近红外光谱结合化学计量学在药物无损定量分析中的应用研究[D].长春：吉林大学,2010,4-6.
[20]Velasco L, Becker H C. Estimating the fatty acid composition of the oil in iIntact-seed rapeseed(Brassica napus L.)by near-infrared reflectance spectroscopy[J]. Euphytica,1998; 101(2):221-230.
[21]任玉林,李伟,沈今明,等.近红外反射光谱法评价安乃近药物质量[J].光谱学与光谱分析,1997；17(3)：51-56.
[22]刘沭华,张学工,孙素琴.中药材产地的近红外光谱自动鉴别和特征波段选择[J].科学通报,2005；50(4)：393-398.
[23]刘沭华,张学工,周群,等.近红外漫反射光谱法和模式识别技术鉴别中药材产地[J].光谱学与光谱分析,2006；26(4)：629-632.
[24]郝勇.近红外光谱微量分析方法研究[D].长春：南开大学,2009,5-7.
[25]贾永.近红外光谱技术在熟地黄饮片质量控制中的应用研究[D].郑州：河南中医学院,2006,38-39.
[26]白雁,龚海燕,宋瑞丽,等.近红外漫反射光谱法快速测定山药药材中水、醇浸出物含量[J].中国现代应用药学,2010；27(2)：163-166.
[27]白雁,龚海燕,宋瑞丽,等.近红外漫反射光谱法快速测定山药药材中多糖的含量[J].中成药,2010；32(1)：110-112.
[28]白雁,宋瑞丽,陈志红,等.NIR结合OPUS软件建立山药中尿囊素定量模型[J].中国医院药学杂志,2008；28(22)：1945-1948.
[29]YANG N L, CHENG Y Y, WU Y J. A new method for fast and nondestructive analysis of saponins in panax notoginseng herb by near infrared spectroscopy[J].Acta Chimica Sinica,2003; 61(3):393-398.
[30]史春香,杨悦武,郭治听,等.近红外技术定量分析丹参药材[J].中药材,2006；29(9)：897-899.
[31]陈雪英,李页瑞,陈勇,等.近红外光谱分析技术在赤芍提取过程质量监控中的应用研究[J].中国中药杂志,2009；34(11)：1355-1358.
[32]王静,莫必琪,李斌,等.近红外光谱法预测红参醇提过程中总皂苷的变化研究[J].中草药,2007,38(9)：1323-1326.
[33]罗晓芳.统计过程控制在丹参注射液生产质量控制中的应用研究[D].杭州：浙江大学,2008,12-18.
[34]杨南林,程翼宇,瞿海斌.一种用于中药纯化过程的近红外光谱分析新方法[J].化学学报,2003；61(5)：742-747.
[35]张延莹,张金巍,张培,等.近红外光谱技术在丹酚酸B纯化在线质控中的应用研究[J].时珍国医国药,2010；21(1)：220-222.
[36]王静.过程分析技术在丹参注射液醇沉和养胃颗粒喷雾制粒中的应用研究[D].杭州：浙江大学,2007,13-20.
[37]徐晓杰,宋丽丽,朱新科,等.近红外光谱法用于六味地黄丸粉末混合过程的质量控制研究[J].中国现代应用药学杂志,2006；23(7)：644-646.
[38]Moes JJ, Ruijken MM, Gout E, et al. Application of process analytical technology in tablet process development using NIR spectroscopy:blend uniformity, content uniformity and coating thickness measurements [J]. International Journal of Pharmaceutics,2008; 357(1-2):108-118.
[39]刘冰,陈晓辉,史新元.近红外光谱法快速测定乳块消糖衣片的包衣厚度[J].药物分析杂志,2009；29(9)：1435-1439.
[40]王宁,魏红,蔡绍松,等.一种快速测定心通口服液中葛根素含量的新方法[J].中成药,2009；32(1)：228-231.
[41]虞科,胡楚楚,程翼宇.近红外光谱法测定复方丹参滴丸中的3种有效成分[J].中国药学杂志,2006；41(3)：226-229.
[42]聂黎行,王钢力,李志猛.近红外光谱法对同仁乌鸡白凤丸的定性和定量分析[J].红外与毫米波学报,2008；27(3)：205-209.
[43]戴传云,高晓燕,汤波,等.近红外光谱法测定双黄连口服液中绿原酸和连翘苷的含量[J].光谱学与光谱分析,2010；30(2)：358-361.
[44]高越,柴逸峰,吴玉田.近红外漫反射光谱法鉴别贝母药材的研究[J].中成药,2005；27(12)：1443-1446.
[45]白雁,王星,陈志红,等.近红外光谱法测定连翘药材中连翘苷含量[J].中成药,2009；31(11)：1755-1759.
[46]FENG Jun-qin, ZHOU Yu-chang, L Hua, etal. Apply diffused reflection NIR technique for themeasurement ofmoisture content in Traditional Chinese Medicine. PopSci,2006; 2:46.
[47]孙振球.医学统计学[M],北京：人民卫生出版社,2002,1：181.
[48]吴伟,崔光华.星点设计-效应面优化法及其在药学中的应用[J].国外医学：药学分册,2000；27(5)：292-298.
[49]韩珍,贺戈.白芍总苷的药理作用及其毒性研究进展[J].宁夏医学院学报,2008；30(4)：538-541.
[50]王巧,刘荣霞,毕开顺,等.HPLC法测定白芍总苷胶囊中芍药内酯苷、芍药苷和苯甲酰芍药苷[J].中草药,2005;36(11)：1630-1632.
[51]胡洪,黄虎,狄留庆.中药喷雾干燥技术的分析与研究进展[J].干燥技术与设备,2009；7(5)：199-205.
[52]黄丹,张强,严芳.紫苏提取物喷雾干燥工艺研究[J].食品与机械,2009； 25(5)：160-162.
[53]李文龙,徐金钟,刘绍勇,等.近红外漫反射光谱法测定5种中药提取物中水分含量的研究[J].药物分析杂志,2009；29(10)：1602-1606.
[54]郝勇.近红外光谱微量分析方法研究[D].天津：南开大学,2009,14-17.
[55]仇法新,高福君.中药的发展现状及应用前景[J].中国药房,2007；18(3)：163-165.
[56]杨家福.中药的研究进展[J].中国医疗前沿,2007；2(19)：30-31.
[57]凌海慧,刘晓飞.中药的研究进展及应用前景分析[J].云南中医学院学报,2009；32(3)：68-70.
[58]王旭英,王翠丽.中药的研究及临床应用[J].中国实用医药,2007；2(29)：71.
[59]陈向东,刘法锦,詹发根,等.中药干法制粒黏合剂的筛选[J].中成药,2008；30(4)：616-618.
[60]汤俊明,孙素琴,袁子民,等.中药红外指纹图谱的无损快速鉴别研究[J].光谱学与光谱分析,2004；24(5)：554-556.
[61]崔景朝.归脾汤单煎与合煎药理作用比较研究[J].药理与临床,1998；14(3)：68.
[62]冯少华,张春红,廖惠芳.苦参颗粒与苦参煎剂药效及毒性的对比性研究[J].中华中医药学刊,2007；25(8)：1602-1604.
[63]潘敏求,杨永华.参苏复方颗粒剂与参苏丸的临床对比观察和实验研究[J].湖南中医杂志,1994；1(5)：7.
[64]王智民,叶祖光,肖诗鹰,等.对中药发展的几点建议和应用前景分析[J].中国中药杂志,2004,29(1)：1-3.
[65]俞翔.丹参提取和醇沉过程的质量控制技术研究[D].杭州：浙江大学,2008,18-20.
[66]陈彬华,文彬.QbD理念在药品研发、生产、质量控制过程中的应用[J].上海医药,2008；29(10)：446-447.
[67]LOYPAERT J, MASSART D L, VANDER HEYDEN Y. Near-infrared spectroscopy applications in pharmaceutical analysis[J]. Talanta,2007; 72 (3): 865-883.
[68]BUCKTON G, YONEMOCHI E, HAMMOND J, et al.The use of near infra-red spectroscopy to detect changes in the form of amorphous and crystalline lactose[J].Int J Pharm,1998; 168 (2):231-241.
[69]OTSUKA M. Comparative particle size determination of phenacetin bμLk powder by using Kubelka-Munk theory and principal component regression analysis based on near-infrared spectroscopy[J]. Powder Technology,2004; 141 (3):244-250.
[70]KRAMER K, EBEL S.Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients[J]. Anal Chim Acta,2000; 420 (2):155-161.
[71]HAILEY P A, DOHERTY P, TAPSELL P, et al. Automated system for the on-line monitoring of powder blending processes using near-infrared spectroscopy Part Ⅰ.System development and control[J].J Pharm Biomed Anal,1996; 14 (5):551-560.
[72]王静.过程分析技术在丹参注射液醇沉和养胃颗粒喷雾制粒中的应用研究[D].杭州：浙江大学,2007,38.
[73]罗晓芳.统计过程控制在丹参注射液生产质量控制中的应用研究[D].杭州：浙江大学,2008,43.
[74]FRAKE P, GREENHALGH D, GRIERSON S M, et al. Process control and end-point determination of a fluid bed gran μ Lation byapplication of near infra-red spectroscopy[J].Int J Pharm,1997; 151 (1):75-80.
[75]DOU Y, HONG M, ZHAO L Z, et al. Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis [J]. Anal Biochem,2006; 351 (2):174-180.
[76]TANG H X, YANG M C, CHEN G L. Sim μ Ltaneous determination of four components in compound methoxyphenamine caps μ Les by NIR diffuse reflectance spectroscopy[J]. Chin J Mod Appl Pharm,2005; 22 (5):50-52.
[77]谢慧军,甘勇,陈庆华.近红外光谱分析技术在制剂领域中的应用[J].中国药学杂志,2009；44(2)：87-91.
[78]杜一平,潘铁英,张玉兰.化学计量学应用[M].北京：化学工业出版社,2008,1：3-4.
[79]吴海.质谱法结合计量学在中药分析及脂组学中的应用研究[D].长沙：中南大学,2009,8-9.
[80]徐会宁.化学计量学方法在药物色谱分析中的应用研究[D].西安：西北大学,2007,14-16.
[81]曾真,程翼宇,沈国芳.一种测定中药复方制剂中活性成分的计算分析方法[J].化学学报,2003；61(1)：84-88.
[82]龚范,彭源贵,崔卉,等.HELP法在中草药分析中的应用研究-冬虫夏草的化学成分测定[J].高等学校化学学报,1999；20(2)：199-203.
[83]李晓如,梁逸曾,杨辉,等.中药药对的化学成分研究川芎-赤芍挥发油的GC/MS分析[J].高等学校化学学报,2006；27(3)：84-88.
[84]卢红梅,梁逸曾.高效液相色谱法测定食物中的类胡萝卜素[J].色谱,2005；23(1)：57-62.
[85]董林,许禄.二维重叠色谱峰解析方法的比较[J].分析化学,2004；32(6)：741-746.
[86]张汉明,曾明,郑水庆,等.中药葛根及同属植物的模式识别研究(Ⅱ)[J].中草药,2001；32(3)：253-254.
[87]张耀奇,潘扬,王天山,等.术类中药及其相关成药质量的主成分分析[J].南京中医药大学学报,1997；13(3)：149-150.
[88]马龄,,王玺.中药吴茱萸中脂肪酸气相色谱数据的化学模式识别[J].计算机与应用化学,2000；17(1)：123.
[89]刘荣霞,周婷婷,董婷霞,等.建立评价当归质量的HPLC指纹图谱分析方法[J].中国药学杂志,2003；38(10)：757-760.
[90]董海娟;张尊建;余静,等.不同产地穿心莲药材色谱指纹图谱的比较研究[J].中成药,2006；28(3)：321-324.
[91]王跃飞,文红梅,郭立玮,等.不同产地甘草的聚类分析[J].中草药,2006；37(3)：435-439.
[92]陈斌,蔡宝昌.大黄药材HPLC指纹图谱的化学模式识别[J].南京中医药大学学报,2006；22(1)：47-50.
[93]周福添,徐树棋,王兆基,等.化学计量学方法应用于中药化学特征指纹图谱[J].中药新药与临床药理,2001；12(3)：164-169.
[94]赵宇,谢培山,梁逸曾,等.中药枳壳HPLC指纹图谱分析及化学模式识别分类研究[J].中国药学杂志,2005；40(11)：812-814.
[95]乔延江,王玺,毕开顺,等.人工神经网络在中药蟾酥化学模式识别特征提取中的应用[J].药学学报,1995；30(9)：698-701.
[96]冯雪松,刘雅茹,张克荣,等.模式识别在白芍质量控制中的应用[J].中药学,2006；37(4)：592-595.
[97]陈四平,张浩,李相冲,等.中药白芍的研究进展[J].承德医学院学报,2008；25(3)：293-296.
[98]http://www.tgzyw.net/read.php?tid=4947(铁杆中医论坛)
[99]Jinyu, JohnE, MagdyI. Lactiflorin a monoterpene glueo-side from Paeonyroot[J]. Phytochemistry,1990; 29(12):3859-3863.
[100]Ikuta A, Kanuta K, Satoke T, etal. Triterpenoids from eallus tissue c μ Ltures of Paeonia species[J]. Phytochemistry,1995; 35(5):1203-1207.
[101]Kamita K, Yoshioka K, Salki Y, etal. Triterpenoids and flavonoids from Paeonia lactiflora[J]. Phytochemistry,1997; 41(1):141-144.
[102]M Nishizawa, T Yamagishi, G Nonaka, et al. Structures of gallotannins in paeoniae radix[J]. Chem Pharm Bull,1980; 28(9):2850-2852.
[103]高小荣,田庚元.白芍化学成分研究进展[J].中国新药杂志,2006；15(6)：416-418.
[104]詹可顺,卫华,魏伟.白芍总苷对小鼠化学性肝损伤的保护作用及机制[J].安徽医科大学学报,2006；41(6)：664-666.
[105]胥方元,何成松,干锦华,等.白芍总苷治疗膝关节骨关节炎[J].中国康复,2004；19(3)：166.
[106]石鹏.白芍总苷对大鼠主动型Heymann肾炎的作用[J].第四军医大学学报,2005；26(18)：1661.
[107]高崇凯,潘华新.白芍总苷粉针剂的抗炎、镇痛作用[J].中药新药与临床药理,2002；13(3)：163-165.
[108]魏毅,张贵平.白芍总苷对血小板功能的影响[J].医学理论与实践,2004；17(8)：871-872.
[109]蒋峰,刘坚白.纤维肌痛综合征疼痛症状临床疗效观察[J].中华风湿病学杂志,2004;8(12)：750-752.
[110]陈勇.白芍总苷合柳氮磺胺吡啶治疗强直性脊柱炎[J].浙江中西医结合杂志,2004；14(10)：605.
[111]傅建斌,史晓飞.白芍总苷治疗早期类风湿关节炎的临床研究[J].中华风湿病学杂志,2004；8(7)：441.
[112]帅宗文,徐建华,魏伟,等.白芍总苷治疗系统性红斑狼疮的初步探讨[J].中华风湿病学杂志,2004；8(7)：441.
[113]王建生.白芍总苷与激素联合治疗肾病综合征疗效观察[J].中华风湿病学杂志,2004;8(7)：443.
[2]Blanco M, Villmroya I. Trends in Analytical Chemistry[J].2002; 21(4): 240-250.
[3]陆婉珍,褚小立.近红外光谱(NIR)和过程分析技术(PAT)[J].现代科学仪器,2007；(4)：13-17.
[4]FDA, "Guidance for Industry:PAT-A Framework for Innovative Pharmac-eutical Development, Manufacturing and Quality Assurance", September 2004.
[5]杨南林.基于近红外光谱的中药过程分析方法研究[D].杭州：浙江大学,2003,3-6.
[6]Callis J. B, lllman R. L., Kowalski B. R., Process analytical chemistry, Anal Chem,1987; 59(9):624A.
[7]沈虹,邓玉霞,吴诚.过程分析化学的提出与进展[J].理化检验-化学分册,1998；34(10)：473-477.
[8]李华,高鸿.过程分析化学的新进展[J].分析化学,2001；29(4)：473-477.
[9]陆婉珍,袁洪福,徐广通,等.现代近红外光谱分析技术[M],北京：中国石化出版社,2007,2：1-11.
[10]Siesler H W, Ozaki Y, Kawata S. Near infrared spectroscopy:Principle instruments and application[M], Weinheim:Wiley-VCH,2002,1:86-88.
[11]Huck C W. Advances of near-infrared spectroscopy in phytochemistry [J].Near Infrared Spec.,2002; 10:491-496.
[12]Ingle J D Jr, Crouch S R. Spectrochemical Analysis[M], New York:Prentice Hall Englewood Cliffs,1988,1:113-114.
[13]Osborne B G, Fearn T. Near Infrared Spectroscopy in Food Analysis[M], New York:John Woley and Sons Inc.,1986,1:135-137.
[14]宋岩.近红外光谱技术在药物检测中的应用研究[D].长春：吉林大学,2009,2-3.
[15]朱韧.近红外在线光谱分析仪的研制[D].杭州：浙江大学,2008,6-7.
[16]田地,金钦汉,近红外光谱仪器[J].分析仪器,2001；(3)：39-42.
[17]徐广通,袁洪福,陆婉珍,近红外光谱仪器概况与进展[J].现代科学仪器,1997；(3)：9-11.
[18]孙宏伟,近红外光谱分析仪器的应用与发展[J].仪器评价,2002；(5)：26-28.
[19]王彬.近红外光谱结合化学计量学在药物无损定量分析中的应用研究[D].长春：吉林大学,2010,4-6.
[20]Velasco L, Becker H C. Estimating the fatty acid composition of the oil in iIntact-seed rapeseed(Brassica napus L.)by near-infrared reflectance spectroscopy[J]. Euphytica,1998; 101(2):221-230.
[21]任玉林,李伟,沈今明,等.近红外反射光谱法评价安乃近药物质量[J].光谱学与光谱分析,1997；17(3)：51-56.
[22]刘沭华,张学工,孙素琴.中药材产地的近红外光谱自动鉴别和特征波段选择[J].科学通报,2005；50(4)：393-398.
[23]刘沭华,张学工,周群,等.近红外漫反射光谱法和模式识别技术鉴别中药材产地[J].光谱学与光谱分析,2006；26(4)：629-632.
[24]郝勇.近红外光谱微量分析方法研究[D].长春：南开大学,2009,5-7.
[25]贾永.近红外光谱技术在熟地黄饮片质量控制中的应用研究[D].郑州：河南中医学院,2006,38-39.
[26]白雁,龚海燕,宋瑞丽,等.近红外漫反射光谱法快速测定山药药材中水、醇浸出物含量[J].中国现代应用药学,2010；27(2)：163-166.
[27]白雁,龚海燕,宋瑞丽,等.近红外漫反射光谱法快速测定山药药材中多糖的含量[J].中成药,2010；32(1)：110-112.
[28]白雁,宋瑞丽,陈志红,等.NIR结合OPUS软件建立山药中尿囊素定量模型[J].中国医院药学杂志,2008；28(22)：1945-1948.
[29]YANG N L, CHENG Y Y, WU Y J. A new method for fast and nondestructive analysis of saponins in panax notoginseng herb by near infrared spectroscopy[J].Acta Chimica Sinica,2003; 61(3):393-398.
[30]史春香,杨悦武,郭治听,等.近红外技术定量分析丹参药材[J].中药材,2006；29(9)：897-899.
[31]陈雪英,李页瑞,陈勇,等.近红外光谱分析技术在赤芍提取过程质量监控中的应用研究[J].中国中药杂志,2009；34(11)：1355-1358.
[32]王静,莫必琪,李斌,等.近红外光谱法预测红参醇提过程中总皂苷的变化研究[J].中草药,2007,38(9)：1323-1326.
[33]罗晓芳.统计过程控制在丹参注射液生产质量控制中的应用研究[D].杭州：浙江大学,2008,12-18.
[34]杨南林,程翼宇,瞿海斌.一种用于中药纯化过程的近红外光谱分析新方法[J].化学学报,2003；61(5)：742-747.
[35]张延莹,张金巍,张培,等.近红外光谱技术在丹酚酸B纯化在线质控中的应用研究[J].时珍国医国药,2010；21(1)：220-222.
[36]王静.过程分析技术在丹参注射液醇沉和养胃颗粒喷雾制粒中的应用研究[D].杭州：浙江大学,2007,13-20.
[37]徐晓杰,宋丽丽,朱新科,等.近红外光谱法用于六味地黄丸粉末混合过程的质量控制研究[J].中国现代应用药学杂志,2006；23(7)：644-646.
[38]Moes JJ, Ruijken MM, Gout E, et al. Application of process analytical technology in tablet process development using NIR spectroscopy:blend uniformity, content uniformity and coating thickness measurements [J]. International Journal of Pharmaceutics,2008; 357(1-2):108-118.
[39]刘冰,陈晓辉,史新元.近红外光谱法快速测定乳块消糖衣片的包衣厚度[J].药物分析杂志,2009；29(9)：1435-1439.
[40]王宁,魏红,蔡绍松,等.一种快速测定心通口服液中葛根素含量的新方法[J].中成药,2009；32(1)：228-231.
[41]虞科,胡楚楚,程翼宇.近红外光谱法测定复方丹参滴丸中的3种有效成分[J].中国药学杂志,2006；41(3)：226-229.
[42]聂黎行,王钢力,李志猛.近红外光谱法对同仁乌鸡白凤丸的定性和定量分析[J].红外与毫米波学报,2008；27(3)：205-209.
[43]戴传云,高晓燕,汤波,等.近红外光谱法测定双黄连口服液中绿原酸和连翘苷的含量[J].光谱学与光谱分析,2010；30(2)：358-361.
[44]高越,柴逸峰,吴玉田.近红外漫反射光谱法鉴别贝母药材的研究[J].中成药,2005；27(12)：1443-1446.
[45]白雁,王星,陈志红,等.近红外光谱法测定连翘药材中连翘苷含量[J].中成药,2009；31(11)：1755-1759.
[46]FENG Jun-qin, ZHOU Yu-chang, L Hua, etal. Apply diffused reflection NIR technique for themeasurement ofmoisture content in Traditional Chinese Medicine. PopSci,2006; 2:46.
[47]孙振球.医学统计学[M],北京：人民卫生出版社,2002,1：181.
[48]吴伟,崔光华.星点设计-效应面优化法及其在药学中的应用[J].国外医学：药学分册,2000；27(5)：292-298.
[49]韩珍,贺戈.白芍总苷的药理作用及其毒性研究进展[J].宁夏医学院学报,2008；30(4)：538-541.
[50]王巧,刘荣霞,毕开顺,等.HPLC法测定白芍总苷胶囊中芍药内酯苷、芍药苷和苯甲酰芍药苷[J].中草药,2005;36(11)：1630-1632.
[51]胡洪,黄虎,狄留庆.中药喷雾干燥技术的分析与研究进展[J].干燥技术与设备,2009；7(5)：199-205.
[52]黄丹,张强,严芳.紫苏提取物喷雾干燥工艺研究[J].食品与机械,2009； 25(5)：160-162.
[53]李文龙,徐金钟,刘绍勇,等.近红外漫反射光谱法测定5种中药提取物中水分含量的研究[J].药物分析杂志,2009；29(10)：1602-1606.
[54]郝勇.近红外光谱微量分析方法研究[D].天津：南开大学,2009,14-17.
[55]仇法新,高福君.中药的发展现状及应用前景[J].中国药房,2007；18(3)：163-165.
[56]杨家福.中药的研究进展[J].中国医疗前沿,2007；2(19)：30-31.
[57]凌海慧,刘晓飞.中药的研究进展及应用前景分析[J].云南中医学院学报,2009；32(3)：68-70.
[58]王旭英,王翠丽.中药的研究及临床应用[J].中国实用医药,2007；2(29)：71.
[59]陈向东,刘法锦,詹发根,等.中药干法制粒黏合剂的筛选[J].中成药,2008；30(4)：616-618.
[60]汤俊明,孙素琴,袁子民,等.中药红外指纹图谱的无损快速鉴别研究[J].光谱学与光谱分析,2004；24(5)：554-556.
[61]崔景朝.归脾汤单煎与合煎药理作用比较研究[J].药理与临床,1998；14(3)：68.
[62]冯少华,张春红,廖惠芳.苦参颗粒与苦参煎剂药效及毒性的对比性研究[J].中华中医药学刊,2007；25(8)：1602-1604.
[63]潘敏求,杨永华.参苏复方颗粒剂与参苏丸的临床对比观察和实验研究[J].湖南中医杂志,1994；1(5)：7.
[64]王智民,叶祖光,肖诗鹰,等.对中药发展的几点建议和应用前景分析[J].中国中药杂志,2004,29(1)：1-3.
[65]俞翔.丹参提取和醇沉过程的质量控制技术研究[D].杭州：浙江大学,2008,18-20.
[66]陈彬华,文彬.QbD理念在药品研发、生产、质量控制过程中的应用[J].上海医药,2008；29(10)：446-447.
[67]LOYPAERT J, MASSART D L, VANDER HEYDEN Y. Near-infrared spectroscopy applications in pharmaceutical analysis[J]. Talanta,2007; 72 (3): 865-883.
[68]BUCKTON G, YONEMOCHI E, HAMMOND J, et al.The use of near infra-red spectroscopy to detect changes in the form of amorphous and crystalline lactose[J].Int J Pharm,1998; 168 (2):231-241.
[69]OTSUKA M. Comparative particle size determination of phenacetin bμLk powder by using Kubelka-Munk theory and principal component regression analysis based on near-infrared spectroscopy[J]. Powder Technology,2004; 141 (3):244-250.
[70]KRAMER K, EBEL S.Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients[J]. Anal Chim Acta,2000; 420 (2):155-161.
[71]HAILEY P A, DOHERTY P, TAPSELL P, et al. Automated system for the on-line monitoring of powder blending processes using near-infrared spectroscopy Part Ⅰ.System development and control[J].J Pharm Biomed Anal,1996; 14 (5):551-560.
[72]王静.过程分析技术在丹参注射液醇沉和养胃颗粒喷雾制粒中的应用研究[D].杭州：浙江大学,2007,38.
[73]罗晓芳.统计过程控制在丹参注射液生产质量控制中的应用研究[D].杭州：浙江大学,2008,43.
[74]FRAKE P, GREENHALGH D, GRIERSON S M, et al. Process control and end-point determination of a fluid bed gran μ Lation byapplication of near infra-red spectroscopy[J].Int J Pharm,1997; 151 (1):75-80.
[75]DOU Y, HONG M, ZHAO L Z, et al. Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis [J]. Anal Biochem,2006; 351 (2):174-180.
[76]TANG H X, YANG M C, CHEN G L. Sim μ Ltaneous determination of four components in compound methoxyphenamine caps μ Les by NIR diffuse reflectance spectroscopy[J]. Chin J Mod Appl Pharm,2005; 22 (5):50-52.
[77]谢慧军,甘勇,陈庆华.近红外光谱分析技术在制剂领域中的应用[J].中国药学杂志,2009；44(2)：87-91.
[78]杜一平,潘铁英,张玉兰.化学计量学应用[M].北京：化学工业出版社,2008,1：3-4.
[79]吴海.质谱法结合计量学在中药分析及脂组学中的应用研究[D].长沙：中南大学,2009,8-9.
[80]徐会宁.化学计量学方法在药物色谱分析中的应用研究[D].西安：西北大学,2007,14-16.
[81]曾真,程翼宇,沈国芳.一种测定中药复方制剂中活性成分的计算分析方法[J].化学学报,2003；61(1)：84-88.
[82]龚范,彭源贵,崔卉,等.HELP法在中草药分析中的应用研究-冬虫夏草的化学成分测定[J].高等学校化学学报,1999；20(2)：199-203.
[83]李晓如,梁逸曾,杨辉,等.中药药对的化学成分研究川芎-赤芍挥发油的GC/MS分析[J].高等学校化学学报,2006；27(3)：84-88.
[84]卢红梅,梁逸曾.高效液相色谱法测定食物中的类胡萝卜素[J].色谱,2005；23(1)：57-62.
[85]董林,许禄.二维重叠色谱峰解析方法的比较[J].分析化学,2004；32(6)：741-746.
[86]张汉明,曾明,郑水庆,等.中药葛根及同属植物的模式识别研究(Ⅱ)[J].中草药,2001；32(3)：253-254.
[87]张耀奇,潘扬,王天山,等.术类中药及其相关成药质量的主成分分析[J].南京中医药大学学报,1997；13(3)：149-150.
[88]马龄,,王玺.中药吴茱萸中脂肪酸气相色谱数据的化学模式识别[J].计算机与应用化学,2000；17(1)：123.
[89]刘荣霞,周婷婷,董婷霞,等.建立评价当归质量的HPLC指纹图谱分析方法[J].中国药学杂志,2003；38(10)：757-760.
[90]董海娟;张尊建;余静,等.不同产地穿心莲药材色谱指纹图谱的比较研究[J].中成药,2006；28(3)：321-324.
[91]王跃飞,文红梅,郭立玮,等.不同产地甘草的聚类分析[J].中草药,2006；37(3)：435-439.
[92]陈斌,蔡宝昌.大黄药材HPLC指纹图谱的化学模式识别[J].南京中医药大学学报,2006；22(1)：47-50.
[93]周福添,徐树棋,王兆基,等.化学计量学方法应用于中药化学特征指纹图谱[J].中药新药与临床药理,2001；12(3)：164-169.
[94]赵宇,谢培山,梁逸曾,等.中药枳壳HPLC指纹图谱分析及化学模式识别分类研究[J].中国药学杂志,2005；40(11)：812-814.
[95]乔延江,王玺,毕开顺,等.人工神经网络在中药蟾酥化学模式识别特征提取中的应用[J].药学学报,1995；30(9)：698-701.
[96]冯雪松,刘雅茹,张克荣,等.模式识别在白芍质量控制中的应用[J].中药学,2006；37(4)：592-595.
[97]陈四平,张浩,李相冲,等.中药白芍的研究进展[J].承德医学院学报,2008；25(3)：293-296.
[98]http://www.tgzyw.net/read.php?tid=4947(铁杆中医论坛)
[99]Jinyu, JohnE, MagdyI. Lactiflorin a monoterpene glueo-side from Paeonyroot[J]. Phytochemistry,1990; 29(12):3859-3863.
[100]Ikuta A, Kanuta K, Satoke T, etal. Triterpenoids from eallus tissue c μ Ltures of Paeonia species[J]. Phytochemistry,1995; 35(5):1203-1207.
[101]Kamita K, Yoshioka K, Salki Y, etal. Triterpenoids and flavonoids from Paeonia lactiflora[J]. Phytochemistry,1997; 41(1):141-144.
[102]M Nishizawa, T Yamagishi, G Nonaka, et al. Structures of gallotannins in paeoniae radix[J]. Chem Pharm Bull,1980; 28(9):2850-2852.
[103]高小荣,田庚元.白芍化学成分研究进展[J].中国新药杂志,2006；15(6)：416-418.
[104]詹可顺,卫华,魏伟.白芍总苷对小鼠化学性肝损伤的保护作用及机制[J].安徽医科大学学报,2006；41(6)：664-666.
[105]胥方元,何成松,干锦华,等.白芍总苷治疗膝关节骨关节炎[J].中国康复,2004；19(3)：166.
[106]石鹏.白芍总苷对大鼠主动型Heymann肾炎的作用[J].第四军医大学学报,2005；26(18)：1661.
[107]高崇凯,潘华新.白芍总苷粉针剂的抗炎、镇痛作用[J].中药新药与临床药理,2002；13(3)：163-165.
[108]魏毅,张贵平.白芍总苷对血小板功能的影响[J].医学理论与实践,2004；17(8)：871-872.
[109]蒋峰,刘坚白.纤维肌痛综合征疼痛症状临床疗效观察[J].中华风湿病学杂志,2004;8(12)：750-752.
[110]陈勇.白芍总苷合柳氮磺胺吡啶治疗强直性脊柱炎[J].浙江中西医结合杂志,2004；14(10)：605.
[111]傅建斌,史晓飞.白芍总苷治疗早期类风湿关节炎的临床研究[J].中华风湿病学杂志,2004；8(7)：441.
[112]帅宗文,徐建华,魏伟,等.白芍总苷治疗系统性红斑狼疮的初步探讨[J].中华风湿病学杂志,2004；8(7)：441.
[113]王建生.白芍总苷与激素联合治疗肾病综合征疗效观察[J].中华风湿病学杂志,2004;8(7)：443.