近红外光谱分析技术在肝素钠生产过程中的应用研究
|
摘要
肝素是糖醛酸和葡萄糖胺及它们的衍生物连接而成的具有不同链长的线性多糖,是开发最早、临床应用最广泛的糖胺聚糖类大分子药物。这类药物能够参与、影响和调控人体代谢和生理功能,临床上主要作为抗凝血药物。此外,它还具有调血脂、抗炎、抗过敏以及免疫调节等重要功能。肝素是我国最重要的生化原料药出口品种之一,占世界肝素原料药贸易份额高达87%。肝素的生产过程主要是指从肝素原料的评价开始一直到肝素成品入库的整个过程。在该过程中,多硫酸软骨素(Oversulfated Chondroitin Sulfate, OSCS)这一肝素结构类似物,能够引起严重的不良反应,应杜绝其掺入；肝素结构的完整性是保持其生物活性的基础,在生产中应尽量避免肝素结构的破坏；与肝素相结合的杂蛋白易产生过敏反应,生产过程中必须尽量去除；醇沉环节是影响肝素产品质量和收率的关键单元操作,应严格控制。因此,寻找精准质量控制方法可为保证肝素钠质量,保障广大人民的用药安全提供重要的技术支持。但实现精准控制的前提是对工艺过程的深刻理解,这就需要有效的过程分析技术做保证。
近红外光谱分析技术(Near-infrared Spectroscopy, NIRS)作为一种快速、有效的过程分析方法,已经在化工等领域的过程分析中验证了其应用价值,但是在肝素钠等糖胺聚糖类药物过程分析中的应用还没有研究。
本文以肝素钠的精制过程为研究对象,利用NIRS和化学计量学方法对肝素钠原料中的杂质OSCS及生产过程中离子交换、醇沉等关键单元操作的快速检测技术进行了研究,并针对肝素钠成品效价的测定进行了近红外光谱波段选择的研究。为近红外光谱法应用于肝素钠生产过程的质量控制奠定了理论基础。
本课题研究内容及取得的主要成果有以下几个方面：
1.NIRS用于肝素原料药中OSCS的快速检测方法研究
本研究采用偏最小二乘β判别(Partial Least Squares Beta Classification, PLSBC)和K-最邻近距离算法(K-nearest Neighbor, KNN)两种模式识别方法分别对93份肝素钠原料药样品(47份不含有OSCS的精品肝素钠样品,46份含有OSCS的肝素钠样品)的近红外光谱进行了定性判别分析,分别选择34份不含有OSCS的精品肝素钠样品和34份含有OSCS的肝素钠样品建立定性分析模型,并利用剩余样品对模型进行验证。PLSBC验证结果表明当OSCS的含量大于0.7%时,可以有效地判别出含有OSCS的肝素钠样品。KNN验证结果表明所建KNN模型能够100%正确判别样品中是否含有OSCS。为进一步验证不同含量OSCS与不含有OSCS的精品肝素钠的关系,将不含有OSCS的精品肝素钠作为A等级,并根据OSCS含量的不同,将含有OSCS的肝素钠样品划分为5个含量等级,分别为B、C、D、E和F, OSCS含量等级判别时模型的判断正确率达83.33%。以上结果表明,运用NIRS可鉴别肝素钠样品中是否含有OSCS,这可为实际生产中原料药中是否含有OSCS提供可靠且快速的检测方法
2. NIRS用于快速监测粗品肝素离子交换层析过程的研究
本研究选取肝素离子交换纯化过程流出液为对象,收集80份吸附过程样品和76份洗脱过程样品,采用偏最小二乘(Partial Least Squares, PLS)算法分别建立吸附过程和洗脱过程流出液中肝素含量的NIRS定量分析模型。通过模型优化,最终选取光谱特征点8666cm-1,7160cm-1,5365cm-1建立吸附过程肝素含量定量分析模型,验证集预测均方根误差(RMSEP)=0.29g/L,验证集相关系数Rp=0.957,验证集标准偏差与预测标准偏差的比值RPD=3.4472；选取6098-5802cm-1这一波段范围建立洗脱过程肝素含量定量分析模型,RMSEP=3.55g/L, Rp=0.9647, RPD=3.9849。并按照样品批次将模型预测含量与参考值进行比较,肝素含量测定的参考值和近红外光谱模型预测值绘制的肝素含量变化趋势基本接近。所建方法具有快速、无损等优点,无需复杂的样品前处理,可直接对流出液样品进行检测,实现层析过程的实时跟踪。
3. NIRS用于醇沉过程中肝素钠含量的测定研究
本研究通过模拟肝素钠的醇沉过程,收集5批共80个醇沉过程样品,离心后取上清,采集上清的近红外光谱并测定其肝素含量。首先采用主成分分析(Principal Component Analysis, PC A)对醇沉过程进行光谱的特征性分析,不同批次之间主成分的变化趋势基本一致,第一主成分随着时间的推移得分不断的增加,揭示了乙醇量的不断增加；第二主成分可以较好的反应醇沉体系的稳定程度,开始向溶液中加入乙醇,肝素大量沉淀出来,体系的稳定性被破坏,在30-40min时,溶液中肝素的含量变化曲线出现拐点,主成分的得分也相应到达拐点；之后溶液中的肝素含量很低,体系又慢慢趋于稳定。结果表明通过PCA得分图的变化规律,可以很好地揭示整个肝素醇沉过程的变化趋势。其次,采用一阶导数SG15点平滑、交叉验证选取3个主成分数,在9034.87-8940.37cm-1、8649.17-8554.68cm-1、7974.21-7686.87cm-1、7395.67-7301.18cm-1以及5949.32-5758.4cm-1的波数范围建立肝素含量的PLS定量分析模型,绝对系数R2=0.974,交互验证均方根误差(RMSECV)=1.4089g/L,用建立的模型对结果进行预测,RMSEP=1.1054g/L。实验结果表明运用NIRS监测醇沉母液中肝素含量准确度和精密度均能满足工厂需要。
4.肝素钠精品效价和近红外光谱之间的关联性研究
本研究对47份效价分布范围在154-162IU/mg的肝素钠精品进行效价与近红外光谱之间的关联性探讨。通过对肝素钠精品的效价与近红外光谱建立PLS模型,预测肝素钠精品的效价,同时考察了相关系数法、间隔偏最小二乘法(interval PLS, iPLS)、连续投影算法(Successive Projections Algorithm, SPA)三种波段选择方法对模型预测能力的影响以及吸光度、效价、结构之间的关系。结果表明相关系数法所选择的波段具有最佳的模型参数,Rp=0.8868, RMSEP=0.7925IU/mg, RMSECV=1.2786IU/mg.相关系数法选择的波段分布范围较广,涵盖C-H键振动的一级倍频、二级倍频、组合频,1800-2100nm的强烈吸收是由于O-H键的组合频引起,这些吸收区间均与肝素的结构有很大相关性,包含了肝素结构的诸多有效信息,因此相关系数法所选择的波段能产生最优的预测结果。
本研究取得的主要创新性成果有以下几个方面：
1.研究建立了利用近红外光谱结合PLSBC法和KNN法鉴别肝素钠原料药中OSCS的方法；
2.研究建立了利用近红外光谱定量监测粗品肝素离子交换过程肝素含量分析方法；
3.研究建立了利用近红外光谱定性和定量分析醇沉过程肝素钠含量变化的方法；
4.对肝素钠精品效价和近红外光谱之间的关联性进行了研究,优化了近红外光谱定量分析肝素钠精品效价的模型。
5.揭示了近红外光谱与肝素在不同体系中的关联规律,并研究了与不同物质之间的差异特征。
Heparin is a linear polysaccharide with a repeating disaccharide unit of1,4-linked uronic acid (D-glucuronic (GlcA) or L-iduronic (IdoA) acid) and D-glucosamine (GlcN) residues. It is a kind of macromolecule glycosaminoglycans (GAGs) drugs which was first developed and used in clinical application. It will participate, affect and regulate metabolism and physiological function. It is mainly used as an anticoagulant in clinical, meanwhile it can regulate the blood fat and can be used as anti-inflammatory, antiallergic drugs and immunomodulator. Heparin, one of the most important biological raw material drug, takes up as high as87%of the world share of trade. The production process of heparin refers to the whole process from evaluation of heparin API to production storage. Oversulfated chondroitin sulfate (OSCS), a contaminant in heparin. can cause serious adverse reactions so thant it should be avoided doped. The biological activity of heparin is based on the integrated structure so that structure destruction should be avoided in the production. In addition, the protein combined with heparin is prone to allergic reactions and it must be removed as much as possible in the production process. As for one of the key unit operations affecting the quality and yield of heparin, the process of alcohol precipitation should be strictly controlled. Therefore, it is great important to find a precise method to ensure the quality of heparin. And a vaild process analytical technology can give a deep understanding the process to precise control of the production process.
Near-infrared spectroscopy (NIRS) analysis technology used as a fast and effective method has been widely used in chemical industry for process monitoring, but there is yet no research paper about near-infrared spectroscopy reported in the application of heparin manufacture process.
Under these consideration, this thesis aimed at reseaech on the discriminant of OSCS in heparin active pharmaceutical ingredient (API), ion exchange process of crude heparin, and the procrss of ethanol precipitation using near-infrared spectroscopy and chemometric. And the relationship between potency of heparin and near-infrared spectra of heparin was also studied. These studies will lay a theoretical foundation for application NIRS on the production process control of heparin.
The results and conclusion of the research about heparin process based on NIRS are listed belows:
1. Research on a rapid detection of OSCS in heparin API by NIRS
In this study, two pattern recognition methods, Partial Least Squares Beta Classification (PLSBC) and K-nearest Neighbor (KNN) were used to qualitative discriminant analysis of93heparin API samples (47heparin samples not containing OSCS,46heparin samples containing OSCS) based on near-infrared spectroscopy.34heparin samples not containing OSCS and34heparin samples containing OSCS were used to establish qualitative analysis model, and the model was validated using the remaining samples. The results of PLSBC indicate that NIRS can detect the OSCS effectively when the content greater than0.7%in heparin. Then a method KNN combined with PCA was investigated in detecting OSCS. The17validation samples were predicted correctly, the accuracy rate was100%. In order to further validate the relationship betwent the heparin samples not containing OSCS and heparin samples containing OSCS, we treated heparin samples not containing OSCS as A level, and divided heparin samples containing OSCS into five different content levels such as B level, C level, D level, E level and F level according to OSCS content. Then the result showed that there were two samples which were predicted wrongly in different OSCS content levels, the accuracy rate in OSCS was83.33%.
2. Application of NIRS in monitoring of crude heparin purification process
Ion-exchange chromatography is a widely used purification technology in the heparin manufacturing process. To improve the efficiency and understand the process directly, a rapid and equally precise method needs to be developed to measure heparin concentration in chromatography process. Here, two robust partial least squares (PLS) models were established for quantification of heparin based on NIRS with80samples of adsorption process and76samples of elution process. Several variables selection algorithms, including correlation coefficient method, successive projection algorithm (SPA) and interval partial least squares (iPLS), were performed to remove non-informative variables. The results showed that the correlation coefficient of validation (Rp) and the residual predictive deviation (RPD) corresponded to0.957and3.4472for adsorption process,0.968and3.9849for elution process, respectively. The approach was found considerable potential for real-time monitoring the heparin concentration of chromatography process.
3. Application of NIRS in determination of heparin content in ethanol precipitation process
Ethanol precipitation process is a key unit operation in the heparin production, and fluctuations of process parameters will lead to an unstable product quality and differences among batches. First, score control charts were established to identify the characteristic trajectory of all batches by Principal Component Analysis (PCA). And the all five batches had a similar trajectory. In addition, the plot showed that the first principal component reflected the content change trend of ethanol, while the second principal component reflected the stability of ethanol precipitation internal system. Then, a PLS model to predict the heparin content in the precipitation process. Five batches of heparin mother liquor were collected under the same ethanol precipitation conditions, and the transmission spectra were associated with the heparin content which was determined by sulfuric acid method. A PLS model was established to predict the content in mother liquor. And the R2was0.974, ang the values of RMSEP and RMSECV was1.1054g/1and1.4089g/1. Throughout the study, NIRS was proved to be an effective tool for determination of heparin content in ethanol precipitation process.
4. Association studies on heparin potency and NIRS
In this study,47heparin samples with pontency among154IU/mg and162IU/mg were investigated to study the association between the potency and NIRS. The PLS model was established to predict the potency and meanwhile the variables effect was investigated to find the ralationship between the absorbance of heparin, potency and the structure of the heparin. Several variables selection algorithms, including correlation coefficient method, SPA and iPLS, were performed to remove non-informative variables. The results showed that the correlation coefficient method having the best model parameters, and the value of Rp was0.8868, while the values of RMSEP and RMSECV was0.7925IU/mg and1.2786IU/mg. A wide distribution of variables was selected by correlation coefficient method, which covered the first overtone, second overtone and some combination overtone of C-H. In addition, the strong absorption of1800-2100nm of was due to the combination of OH bond. And these selected had great correlation with the structure of heparin, so the variables selected by the correlation coefficient method was able to produce the priority prediction results.
Major innovative achievements:
1. Established a method to identify OSCS in heparin API using NIRS combined with PLSBC and KNN.
2. Established a method to monitor the heparin content in the ion-exchange process of crude heparin using NIRS combined with multivariate analysis.
3. Established a method to qualitative and quantitative analysis of heparin content in ethanol precipitation process
4. Studied the association between the heparin potency and NIRS, and optimized NIRS quantitative analysis model of heparin potency.
5. Revealed the associated regularity between NIRS and heparin in the different systems, and studied the different characteristics among substances.
近红外光谱分析技术(Near-infrared Spectroscopy, NIRS)作为一种快速、有效的过程分析方法,已经在化工等领域的过程分析中验证了其应用价值,但是在肝素钠等糖胺聚糖类药物过程分析中的应用还没有研究。
本文以肝素钠的精制过程为研究对象,利用NIRS和化学计量学方法对肝素钠原料中的杂质OSCS及生产过程中离子交换、醇沉等关键单元操作的快速检测技术进行了研究,并针对肝素钠成品效价的测定进行了近红外光谱波段选择的研究。为近红外光谱法应用于肝素钠生产过程的质量控制奠定了理论基础。
本课题研究内容及取得的主要成果有以下几个方面：
1.NIRS用于肝素原料药中OSCS的快速检测方法研究
本研究采用偏最小二乘β判别(Partial Least Squares Beta Classification, PLSBC)和K-最邻近距离算法(K-nearest Neighbor, KNN)两种模式识别方法分别对93份肝素钠原料药样品(47份不含有OSCS的精品肝素钠样品,46份含有OSCS的肝素钠样品)的近红外光谱进行了定性判别分析,分别选择34份不含有OSCS的精品肝素钠样品和34份含有OSCS的肝素钠样品建立定性分析模型,并利用剩余样品对模型进行验证。PLSBC验证结果表明当OSCS的含量大于0.7%时,可以有效地判别出含有OSCS的肝素钠样品。KNN验证结果表明所建KNN模型能够100%正确判别样品中是否含有OSCS。为进一步验证不同含量OSCS与不含有OSCS的精品肝素钠的关系,将不含有OSCS的精品肝素钠作为A等级,并根据OSCS含量的不同,将含有OSCS的肝素钠样品划分为5个含量等级,分别为B、C、D、E和F, OSCS含量等级判别时模型的判断正确率达83.33%。以上结果表明,运用NIRS可鉴别肝素钠样品中是否含有OSCS,这可为实际生产中原料药中是否含有OSCS提供可靠且快速的检测方法
2. NIRS用于快速监测粗品肝素离子交换层析过程的研究
本研究选取肝素离子交换纯化过程流出液为对象,收集80份吸附过程样品和76份洗脱过程样品,采用偏最小二乘(Partial Least Squares, PLS)算法分别建立吸附过程和洗脱过程流出液中肝素含量的NIRS定量分析模型。通过模型优化,最终选取光谱特征点8666cm-1,7160cm-1,5365cm-1建立吸附过程肝素含量定量分析模型,验证集预测均方根误差(RMSEP)=0.29g/L,验证集相关系数Rp=0.957,验证集标准偏差与预测标准偏差的比值RPD=3.4472；选取6098-5802cm-1这一波段范围建立洗脱过程肝素含量定量分析模型,RMSEP=3.55g/L, Rp=0.9647, RPD=3.9849。并按照样品批次将模型预测含量与参考值进行比较,肝素含量测定的参考值和近红外光谱模型预测值绘制的肝素含量变化趋势基本接近。所建方法具有快速、无损等优点,无需复杂的样品前处理,可直接对流出液样品进行检测,实现层析过程的实时跟踪。
3. NIRS用于醇沉过程中肝素钠含量的测定研究
本研究通过模拟肝素钠的醇沉过程,收集5批共80个醇沉过程样品,离心后取上清,采集上清的近红外光谱并测定其肝素含量。首先采用主成分分析(Principal Component Analysis, PC A)对醇沉过程进行光谱的特征性分析,不同批次之间主成分的变化趋势基本一致,第一主成分随着时间的推移得分不断的增加,揭示了乙醇量的不断增加；第二主成分可以较好的反应醇沉体系的稳定程度,开始向溶液中加入乙醇,肝素大量沉淀出来,体系的稳定性被破坏,在30-40min时,溶液中肝素的含量变化曲线出现拐点,主成分的得分也相应到达拐点；之后溶液中的肝素含量很低,体系又慢慢趋于稳定。结果表明通过PCA得分图的变化规律,可以很好地揭示整个肝素醇沉过程的变化趋势。其次,采用一阶导数SG15点平滑、交叉验证选取3个主成分数,在9034.87-8940.37cm-1、8649.17-8554.68cm-1、7974.21-7686.87cm-1、7395.67-7301.18cm-1以及5949.32-5758.4cm-1的波数范围建立肝素含量的PLS定量分析模型,绝对系数R2=0.974,交互验证均方根误差(RMSECV)=1.4089g/L,用建立的模型对结果进行预测,RMSEP=1.1054g/L。实验结果表明运用NIRS监测醇沉母液中肝素含量准确度和精密度均能满足工厂需要。
4.肝素钠精品效价和近红外光谱之间的关联性研究
本研究对47份效价分布范围在154-162IU/mg的肝素钠精品进行效价与近红外光谱之间的关联性探讨。通过对肝素钠精品的效价与近红外光谱建立PLS模型,预测肝素钠精品的效价,同时考察了相关系数法、间隔偏最小二乘法(interval PLS, iPLS)、连续投影算法(Successive Projections Algorithm, SPA)三种波段选择方法对模型预测能力的影响以及吸光度、效价、结构之间的关系。结果表明相关系数法所选择的波段具有最佳的模型参数,Rp=0.8868, RMSEP=0.7925IU/mg, RMSECV=1.2786IU/mg.相关系数法选择的波段分布范围较广,涵盖C-H键振动的一级倍频、二级倍频、组合频,1800-2100nm的强烈吸收是由于O-H键的组合频引起,这些吸收区间均与肝素的结构有很大相关性,包含了肝素结构的诸多有效信息,因此相关系数法所选择的波段能产生最优的预测结果。
本研究取得的主要创新性成果有以下几个方面：
1.研究建立了利用近红外光谱结合PLSBC法和KNN法鉴别肝素钠原料药中OSCS的方法；
2.研究建立了利用近红外光谱定量监测粗品肝素离子交换过程肝素含量分析方法；
3.研究建立了利用近红外光谱定性和定量分析醇沉过程肝素钠含量变化的方法；
4.对肝素钠精品效价和近红外光谱之间的关联性进行了研究,优化了近红外光谱定量分析肝素钠精品效价的模型。
5.揭示了近红外光谱与肝素在不同体系中的关联规律,并研究了与不同物质之间的差异特征。
Heparin is a linear polysaccharide with a repeating disaccharide unit of1,4-linked uronic acid (D-glucuronic (GlcA) or L-iduronic (IdoA) acid) and D-glucosamine (GlcN) residues. It is a kind of macromolecule glycosaminoglycans (GAGs) drugs which was first developed and used in clinical application. It will participate, affect and regulate metabolism and physiological function. It is mainly used as an anticoagulant in clinical, meanwhile it can regulate the blood fat and can be used as anti-inflammatory, antiallergic drugs and immunomodulator. Heparin, one of the most important biological raw material drug, takes up as high as87%of the world share of trade. The production process of heparin refers to the whole process from evaluation of heparin API to production storage. Oversulfated chondroitin sulfate (OSCS), a contaminant in heparin. can cause serious adverse reactions so thant it should be avoided doped. The biological activity of heparin is based on the integrated structure so that structure destruction should be avoided in the production. In addition, the protein combined with heparin is prone to allergic reactions and it must be removed as much as possible in the production process. As for one of the key unit operations affecting the quality and yield of heparin, the process of alcohol precipitation should be strictly controlled. Therefore, it is great important to find a precise method to ensure the quality of heparin. And a vaild process analytical technology can give a deep understanding the process to precise control of the production process.
Near-infrared spectroscopy (NIRS) analysis technology used as a fast and effective method has been widely used in chemical industry for process monitoring, but there is yet no research paper about near-infrared spectroscopy reported in the application of heparin manufacture process.
Under these consideration, this thesis aimed at reseaech on the discriminant of OSCS in heparin active pharmaceutical ingredient (API), ion exchange process of crude heparin, and the procrss of ethanol precipitation using near-infrared spectroscopy and chemometric. And the relationship between potency of heparin and near-infrared spectra of heparin was also studied. These studies will lay a theoretical foundation for application NIRS on the production process control of heparin.
The results and conclusion of the research about heparin process based on NIRS are listed belows:
1. Research on a rapid detection of OSCS in heparin API by NIRS
In this study, two pattern recognition methods, Partial Least Squares Beta Classification (PLSBC) and K-nearest Neighbor (KNN) were used to qualitative discriminant analysis of93heparin API samples (47heparin samples not containing OSCS,46heparin samples containing OSCS) based on near-infrared spectroscopy.34heparin samples not containing OSCS and34heparin samples containing OSCS were used to establish qualitative analysis model, and the model was validated using the remaining samples. The results of PLSBC indicate that NIRS can detect the OSCS effectively when the content greater than0.7%in heparin. Then a method KNN combined with PCA was investigated in detecting OSCS. The17validation samples were predicted correctly, the accuracy rate was100%. In order to further validate the relationship betwent the heparin samples not containing OSCS and heparin samples containing OSCS, we treated heparin samples not containing OSCS as A level, and divided heparin samples containing OSCS into five different content levels such as B level, C level, D level, E level and F level according to OSCS content. Then the result showed that there were two samples which were predicted wrongly in different OSCS content levels, the accuracy rate in OSCS was83.33%.
2. Application of NIRS in monitoring of crude heparin purification process
Ion-exchange chromatography is a widely used purification technology in the heparin manufacturing process. To improve the efficiency and understand the process directly, a rapid and equally precise method needs to be developed to measure heparin concentration in chromatography process. Here, two robust partial least squares (PLS) models were established for quantification of heparin based on NIRS with80samples of adsorption process and76samples of elution process. Several variables selection algorithms, including correlation coefficient method, successive projection algorithm (SPA) and interval partial least squares (iPLS), were performed to remove non-informative variables. The results showed that the correlation coefficient of validation (Rp) and the residual predictive deviation (RPD) corresponded to0.957and3.4472for adsorption process,0.968and3.9849for elution process, respectively. The approach was found considerable potential for real-time monitoring the heparin concentration of chromatography process.
3. Application of NIRS in determination of heparin content in ethanol precipitation process
Ethanol precipitation process is a key unit operation in the heparin production, and fluctuations of process parameters will lead to an unstable product quality and differences among batches. First, score control charts were established to identify the characteristic trajectory of all batches by Principal Component Analysis (PCA). And the all five batches had a similar trajectory. In addition, the plot showed that the first principal component reflected the content change trend of ethanol, while the second principal component reflected the stability of ethanol precipitation internal system. Then, a PLS model to predict the heparin content in the precipitation process. Five batches of heparin mother liquor were collected under the same ethanol precipitation conditions, and the transmission spectra were associated with the heparin content which was determined by sulfuric acid method. A PLS model was established to predict the content in mother liquor. And the R2was0.974, ang the values of RMSEP and RMSECV was1.1054g/1and1.4089g/1. Throughout the study, NIRS was proved to be an effective tool for determination of heparin content in ethanol precipitation process.
4. Association studies on heparin potency and NIRS
In this study,47heparin samples with pontency among154IU/mg and162IU/mg were investigated to study the association between the potency and NIRS. The PLS model was established to predict the potency and meanwhile the variables effect was investigated to find the ralationship between the absorbance of heparin, potency and the structure of the heparin. Several variables selection algorithms, including correlation coefficient method, SPA and iPLS, were performed to remove non-informative variables. The results showed that the correlation coefficient method having the best model parameters, and the value of Rp was0.8868, while the values of RMSEP and RMSECV was0.7925IU/mg and1.2786IU/mg. A wide distribution of variables was selected by correlation coefficient method, which covered the first overtone, second overtone and some combination overtone of C-H. In addition, the strong absorption of1800-2100nm of was due to the combination of OH bond. And these selected had great correlation with the structure of heparin, so the variables selected by the correlation coefficient method was able to produce the priority prediction results.
Major innovative achievements:
1. Established a method to identify OSCS in heparin API using NIRS combined with PLSBC and KNN.
2. Established a method to monitor the heparin content in the ion-exchange process of crude heparin using NIRS combined with multivariate analysis.
3. Established a method to qualitative and quantitative analysis of heparin content in ethanol precipitation process
4. Studied the association between the heparin potency and NIRS, and optimized NIRS quantitative analysis model of heparin potency.
5. Revealed the associated regularity between NIRS and heparin in the different systems, and studied the different characteristics among substances.
引文
[1]CASU B. Structure of heparin and heparin fragments [J]. Ann N Y Acad Sci, 2006,556(Heparin and Related Polysaccharides Structure and Activities):1-17.
[2]NOTI C, SEEBERGER P H. Chemical approaches to define the structure-activity relationship of heparin-like glycosaminoglycans [J]. Chem Biol, 2005,12(7):731-756.
[3]JIN L, ABRAHAMS J P, SKINNER R, et al. The anticoagulant activation of antithrombin by□ heparin [J]. Proc Natl Acad Sci USA,1997,94(26): 14683-14688.
[4]周先碗,何伟.酶降解法精制肝素钠[J].中国生物化学与分子生物学报,1999,15(3)：488-490.
[5]GREEN J P. Fractionation of Heparin on an Anion Exchanger [J]. Nature,1960, 186(4723):472.
[6]朱祖恒,丰树山.离子交换法精制肝素的研究[J].江苏化工,1998,26(1)：18-21.
[7]张万忠.肝素精制及低分子量肝素的制备[D].北京：北京化工大学生物工程系.2002.
[8]孙笛,袁佳.胡晓雁,等.一种基于近红外光谱的华蟾素醇沉过程指标成分及其转移率快速测定方法[J].中国中药杂志,2011,36(18)：2479-2483.
[9]VOLPI N. Fractionation of heparin, dermatan sulfate, and chondroitin sulfate by sequential precipitation:a method to purify a single glycosaminoglycan species from a mixture [J]. Anal Biochem,1994,218(2):382-391.
[10]VOLPI N. Purification of heparin, dermatan sulfate and chondroitin sulfate from mixtures by sequential precipitation with various organic solvents [J]. J Chromatogr B Biomed Sci Appl,1996,685(1):27-34.
[11]刘纯慧,奚涛,林亲雄,等.海胆黄多糖的分离,纯化及免疫活性测定[J].中国海洋药物杂志,2006,25(3)：7-11.
[12]蒋俊,罗珍,刘艳芳,等.醇沉条件对猴头菌多糖得率和品质的影响[J].食用菌学报,2011,18(2)：57-61.
[13]Guidance for Industty Heparin for Druy and Medical Device Use:Monitoring Crude Heparin for Quality [M]. FDA.2012.
[14]DOMANIG R, JOBSTL W, GRUBER S, et al. One-dimensional cellulose acetate plate electrophoresis--a feasible method for analysis of dermatan sulfate and other glycosaminoglycan impurities in pharmaceutical heparin [J]. J Pharm Biomed Anal,2009,49(1):151-155.
[15]VOLPI N, MACCARI F. Electrophoretic approaches to the analysis of complex polysaccharides [J]. J Chromatogr B Analyt Technol Biomed Life Sci,2006, 834(1-2):1-13.
[16]VOLPI N, MACCARI F, LINHARDT R J. Capillary electrophoresis of complex natural polysaccharides [J]. Electrophoresis,2008,29(15):3095-3106.
[17]NORWIG J, BEYER T, BRINZ D, et al. Prediction of the oversulphated chondroitin sulphate contamination of unfractionated heparin by ATR-IR spectrophotometry [J]. Pharmeur Sci Notes,2009(1):17-24.
[18]BIGLER P, BRENNEISEN R. Improved impurity fingerprinting of heparin by high resolution (1)H NMR spectroscopy [J]. J Pharm Biomed Anal,2009,49(4): 1060-1064.
[19]WALENGA J M, HOPPENSTEADT D A. Monitoring the new antithrombotic drugs [J]. Semin Thromb Hemost,2004.30(6):683-695.
[20]赵兴波,曹梦,张晓军.一种鉴定肝素制品动物物种来源的方法,CN101792804 A[P].2010.
[21]黄喜茹,王云志.用天青Ⅰ测定肝素钠注射液的效价[J].河北医学院学报，1994,15(3)：140-142.
[22]刘秀英,张超灿，徐卫林.甲苯胺蓝分光光度法测定肝素钠的研究[J].化学试剂,2009,31(4)：271-274.
[23]季钟煜,蒋传葵.肝素的简易化学测定法[J].生物化学与生物物理进展,1980,5：61-63.
[24]王磊,焦楠.羊血浆法测定海普林软膏中肝素钠生物效价的探讨[J].中国生化药物杂志,2002,23(3)：149-150.
[25]江燕,张力跃.山羊血浆代替绵羊血浆测定肝素钠效价[J].中国生化药物杂志,2000,21(5)：251.
[26]李京,范慧红.微量生色底物法测定肝素钠抗Ⅹa因子和抗Ⅱa因子活性[J].中国生化药物杂志,2010,31(1)：45-47.
[27]谢继青,于燕莉,黄元,等.应用分光光度法测定低分子肝素效价[M].中国药学会学术年会.2002.
[28]江燕,张力跃.粗品肝素钠质量情况和效价范围的判断[J].氨基酸和生物资源,2000,22(2)：16-18.
[29]江燕,张力跃.肝素钠生产中间产物效价的检测[J].中国医药工业杂志,2000,31(11)：494-496.
[30]蔡晨波.近红外基础研究：在线分析,多组份分析和空间效应[D].湖南大学,2008.
[31]ICH. ICH Harmonised tripartite guideline:Q8(R1) pharmaceutical development [M].2008.
[32]READ E K, PARK J T, SHAH R B, et al. Process analytical technology (PAT) for biopharmaceutical products:Part I. concepts and applications [J]. Biotechnol Bioeng,2010,105(2):276-284.
[33]RATHORE A, BHAMBURE R, GHARE V. Process analytical technology (PAT) for biopharmaceutical products [J]. Anal Bioanal Chem,2010,398(1):137-154.
[34]READ E, SHAH R, RILEY B, et al. Process analytical technology (PAT) for biopharmaceutical products:Part II. Concepts and applications [J]. Biotechnol Bioeng,2010,105(2):285-295.
[35]SEASHOLTZ M B. Making money with chemometrics [J]. Chemometr Intell Lab,1999,45(1-2):55-63.
[36]EL-HAGRASY A S, D'AMICO F, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending. Part I:D-optimal design for characterization of powder mixing and preliminary spectral data evaluation [J]. J Pharm Sci,2006,95(2):392-406.
[37]EL-HAGRASY A S, DELGADO-LOPEZ M, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending:Part Ⅱ:Qualitative near-infrared models for prediction of blend homogeneity [J]. J Pharm Sci,2006,95(2):407-421.
[38]EL-HAGRASY A S, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending. Part III: Quantitative near-infrared calibration for prediction of blend homogeneity and characterization of powder mixing kinetics [J]. J Pharm Sci,2006,95(2): 422-444.
[39]MARK J, KARNER M, ANDRE M, et al. Online process control of a pharmaceutical intermediate in a fluidized-bed drier environment using near-infrared spectroscopy [J]. Anal Chem,2010,82(10):4209-4215.
[40]HALSTENSEN M, DE BAKKER P, ESBENSEN K H. Acoustic chemometric monitoring of an industrial granulation production process—a PAT feasibility study [J]. Chemometr Intell Lab,2006,84(1-2):88-97.
[41]KIRSCH J D, DRENNEN J K. Determination of film-coated tablet parameters by near-infrared spectroscopy [J]. J Pharm Biomed Anal,1995,13(10): 1273-1281.
[42]CHAN C-M, LO W, WONG K-Y. Application of a luminescence-based pH optrode to monitoring of fermentation by Klebsiella pneumoniae [J]. Biosens Bioelectron,2000,15(1-2):7-11.
[43]CHEN Y, KROL J, HUANG W, et al. DCO2 on-line measurement used in rapamycin fed-batch fermentation process [J]. Process Biochem 2008,43(4): 351-355.
[44]HEINZLE E, OEGGERLI A, DETTWILER B. On-line fermentation gas analysis:Error analysis and application of mass spectrometry [J]. Analytica Chimica Acta,1990.238(0):101-115.
[45]HUN-CHI S, H KANSON H, MATTIASSON B. On-line monitoring of D-lactic acid during a fermentation process using immobilized D-lactate dehydrogenase in a sequential injection analysis system [J]. Analytica Chimica Acta.1995. 300(1-3):277-285.
[46]ZHANG J, ZHOU H, JI Z, et al. Monoclonal antibody production with on-line harvesting and process monitoring [J]. J Chromatogr B Biomed Sci Appl,1998. 707(1-2):257-265.
[47]BRAD S. Process analytical technology:A strategy for keeping manufacturing viable in Australia [J]. Vib Spectrosc,2007,44(1):171-178.
[48]ROGGO Y, CHALUS P. MAURER L, et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies [J]. J Pharm Biomed Anal,2007,44(3):683-700.
[49]褚小立.化学计量学方法与分子光谱分析技术[M].北京：化学工业出版社.2011.
[50]BLANCO M, COELLO J, ITURRIAGA H, et al. Near-infrared spectroscopy in the pharmaceutical industry. Critical Review [J]. Analyst,1998,123(8): 135R-150R.
[51]VAN DER VLIES C. The use of NIR spectrometry in the pharmaceutical QC laboratory [J]. Eur J Pharm Sci,1994,2(1-2):79-81.
[52]EBUBE N K, THOSAR S S, ROBERTS R A, et al. Application of near-infrared spectroscopy for nondestructive analysis of Avicel powders and tablets [J]. Pharm Dev Technol,1999,4(1):19-26.
[53]KREFT K, KOZAMERNIK B, URLEB U. Qualitative determination of polyvinylpyrrolidone type by near-infrared spectrometry [J]. Int J Pharm,1999, 177(1):1-6.
[54]LI YOON W, D. JEE R, C. MOFFAT A, et al. Construction and transferability of a spectral library for the identification of common solvents by near-infrared transflectance spectroscopy [J]. Analyst,1999,124(8):1197-203.
[55]DE MAESSCHALCK R, VAN DEN KERKHOF T. Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets [J]. J Pharm Biomed Anal,2005,37(1):109-114.
[56]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.
[57]LEE H E, LEE M J, KIM W S, et al. In-line monitoring and interpretation of an indomethacin anti-solvent crystallization process by near-infrared spectroscopy (NIRS) [J]. Int J Pharm,2011,420(2):274-281.
[58]MORISSEAU K M, RHODES C T. Near-infrared spectroscopy as a nondestructive alternative to conventional tablet hardness testing [J]. Pharm Res, 1997,14(1):108-111.
[59]KIRSCH J D, DRENNEN J K. Nondestructive tablet hardness testing by near-infrared spectroscopy:a new and robust spectral best-fit algorithm [J]. J Pharm Biomed Anal,1999,19(3-4):351-362.
[60]BLANCO M, ALCAL M. Content uniformity and tablet hardness testing of intact pharmaceutical tablets by near infrared spectroscopy:A contribution to process analytical technologies [J]. Analytica Chimica Acta,2006,557(1-2): 353-359.
[61]BLANCO M, ALCAL M, GONZ LEZ J M, et al. A process analytical technology approach based on near infrared spectroscopy:Tablet hardness, content uniformity, and dissolution test measurements of intact tablets [J]. J Pharm Sci,2006,95(10):2137-2144.
[62]HATTORI Y, OTSUKA M. NIR spectroscopic study of the dissolution process in pharmaceutical tablets [J]. Vib Spectrosc,2011,57:275-281.
[63]BERNTSSON O, BURGER T, FOLESTAD S, et al. Effective sample size in diffuse reflectance near-IR spectrometry [J]. Anal Chem,1999,71(3):617-623.
[64]RANTANEN J, ANTIKAINEN O, MANNERMAA J P, et al. Use of the near-infrared reflectance method for measurement of moisture content during granulation [J]. Pharm Dev Technol,2000,5(2):209-217.
[65]ZHENG Y, LAI X, BRUUN S W, et al. Determination of moisture content of lyophilized allergen vaccines by NIR spectroscopy [J]. J Pharm Biomed Anal, 2008,46(3):592-596.
[66]LI Y, FAN Q, LIU S, et al. Simultaneous analysis of moisture, active component and cake structure of lyophilized powder for injection with diffuse reflectance FT-NIR chemometrics [J]. J Pharm Biomed Anal,2011,55(1):216-219.
[67]SARRAGUCA M C, SOARES S O, LOPES J A. A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations [J]. Vib Spectrosc,2011,56:184-192.
[68]LIU Y Y, HU C Q. Construction of predictive models for gentamicin contents in aqueous solution using specific near infrared spectral regions [J]. Vibrational Spectroscopy,2010,55(2):241-249.
[69]DONG Q, ZANG H C, LIU A H, et al. Determination of molecular weight of hyaluronic acid by near-infrared spectroscopy [J]. J Pharm Biomed Anal,2010, 53:274-278.
[70]SUN C X, ZANG H C, LIU X M, et al. Determination of potency of heparin active pharmaceutical ingredient by near infrared spectroscopy [J]. J Pharm Biomed Anal,2010,51:1060-1063.
[71]HUANG H, QU H. In-line monitoring of alcohol precipitation by near-infrared spectroscopy in conjunction with multivariate batch modeling [J]. Analytica Chimica Acta,2011,707(1-2):47-56.
[72]WARGO D J, DRENNEN J K. Near-infrared spectroscopic characterization of pharmaceutical powder blends [J]. J Pharm Biomed Anal,1996,14(11): 1415-1423.
[73]COGDILL R, ANDERSON C, DELGADO-LOPEZ M, et al. Process analytical technology case study part Ⅰ:Feasibility studies for quantitative near-infrared method development [J]. AAPS PharmSciTech,2005,6(2):E262-E272.
[74]COGDILL R, ANDERSON C, DELGADO M, et al. Process analytical technology case study:Part Ⅱ. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release [J]. AAPS PharmSciTech,2005,6(2): E273-E283.
[75]COGDILL R, ANDERSON C, DRENNEN J. Process analytical technology case study, part III:Calibration monitoring and transfer [J]. AAPS PharmSciTech, 2005,6(2):E284-E297.
[76]CHABLANI L, TAYLOR M K, MEHROTRA A, et al. Inline real-time nearinfrared granule moisture measurements of a continuous granulation-drying-milling process [J]. AAPS PharmSciTech,2011, 12(4):1050-1055.
[77]DE BEER T, VERCRUYSSE P, BURGGRAEVE A, et al. In-line and real time process monitoring of a freeze drying process using Raman and NIR spectroscopy as complementary process analytical technology (PAT) tools [J]. J Pharm Sci,2009,98(9):3430-3446.
[78]LI W, WOROSILA G D, WANG W, et al. Determination of polymorph conversion of an active pharmaceutical ingredient in wet granulation using NIR calibration models generated from the premix blends [J]. J Pharm Sci,2005, 94(12):2800-2806.
[79]MOLTGEN C V, PUCHERT T, MENEZES J C, et al. A novel in-line NIR spectroscopy application for the monitoring of tablet film coating in an industrial scale process [J]. Talanta,2012,92:26-37.
[80]LEE M J, SEO D Y, LEE H E, et al. In line NIR quantification of film thickness on pharmaceutical pellets during a fluid bed coating process [J]. Int J Pharm, 2011,403(1-2):66-72.
[81]XIE L-G, SUN H-M, JIN S-H. Screening adulteration of polypropylene bottles with postconsumer recycled plastics for oral drug package by near-infrared spectroscopy [J]. Analytica Chimica Acta,2011,706(2):312-320.
[82]CAVINATO A G, MA YES D M, GE Z H, et al. Noninvasive method for monitoring ethanol in fermentation processes using fiber-optic near-infrared spectroscopy [J]. Anal Chem,1990,62(18):1977-1982.
[83]MORITA H, HASUNUMA T, VASSILEVA M, et al. Near infrared spectroscopy as high-throughput technology for screening of xylose-fermenting recombinant Saccharomyces cerevisiae strains [J]. Anal Chem,2011,83(11): 4023-4029.
[84]LIC NIA O. RODRIGUESB L V, JOAQUIM P. CARDOSOA, JOS C. MENEZES. The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems [J]. Talanta,2008,75(5(15)):1356-1361
[85]ARNOLD S A, CROWLEY J, WOODS N, et al. In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation [J]. Biotechnol Bioeng,2003,84(1):13-19.
[86]PIETERS S, DE BEER T, KASPER J C, et al. Near-infrared spectroscopy for in-Line monitoring of protein unfolding and its interactions with lyoprotectants during freeze-drying [J]. Ana Chem,2011,84(2):947-955.
[87]SCARFF M, ARNOLD S A, HARVEY L M, et al. Near infrared spectroscopy for bioprocess monitoring and control:current status and future trends [J]. Crit Rev Biotechnol,2006,26(1):17-39.
[88]BURNS D A, CIURCZAK E W. Handbook of near-infrared analysis[M]. CRC Press.2008.
[89]BRIDGES C C. Hierarchical cluster analysis [J]. Psychol Rep,1966,18(3): 851-854.
[90]FOOT M, MULHOLLAND M. Classification of chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride and glucosamine 6 sulfate using chemometric techniques [J]. J Pharm Biomed Anal,2005,38(3):397-407.
[91]MASSART D L. Chemometrics:a textbook [M]. Elsevier Science,1988.
[92]MARK H L, TUNNELL D. Qualitative near-infrared reflectance analysis using Mahalanobis distances [J]. Anal Chem 1985,57(7):1449-1456.
[93]SHARAF M A, ILLMAN D L. KOWALSKI B R. Chemometrics [M]. Wiley-Interscience,1986.
[94]DERDE M P, BUYDENS L, GUNS C, et al. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data [J]. Anal Chem,1987,59(14):1868-1671.
[95]WOLD S. Pattern recognition by means of disjoint principal components models [J]. Pattern Recognition,1976,8(3):127-139.
[96]ST HLE L, WOLD S. Partial least squares analysis with cross-validation for the two-class problem:A Monte Carlo study [J]. J Chemometr,1987,1(3):185-196.
[97]LEARDI R. Nature-inspired methods in chemometrics:genetic algorithms and artificial neural networks [M]. Elsevier Science,2003.
[98]ZANG H, LI L, WANG F, et al. A method for identifying the origin of chondroitin sulfate with near infrared spectroscopy [J]. J Pharm Biomed Anal, 2012,61:224-229.
[99]N S T, ISAKSSON T, FEARN T, et al. A user friendly guide to multivariate calibration and classification [M]. NIR Publications,2002.
[100]ADAMS M J. Chemometrics in analytical spectroscopy [M]. Springer Verlag, 2004.
[101]BALABIN R M, LOMAKINA E I, SAFIEVA R Z. Neural network (ANN) approach to biodiesel analysis:Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy [J]. Fuel, 2011,90(5):2007-2015.
[102]BALABIN R M, LOMAKINA E I. Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data [J]. Analyst,2011,136(8):1703-1712.
[103]PONTES M J C, GALV O R K H, ARA JO M C U, et al. The successive projections algorithm for spectral variable selection in classification problems [J]. Chemometr Intell Lab,2005,78(1-2):11-18.
[104]ARA JO M C U, SALDANHA T C B, GALV O R K H,et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis [J]. Chemometr Intell Lab,2001,57(2):65-73.
[105]CENTNER V, MASSART D-L, DE NOORD O E, et al. Elimination of uninformative variables for multivariate calibration [J]. Anal Chem,1996,68(21): 3851-3858.
[106]LEARDI R, LUPI EZ GONZ LEZ A. Genetic algorithms applied to feature selection in PLS regression:how and when to use them [J]. Chemometr Intell Lab,1998,41(2):195-207.
[107]NORGAARD L, SAUDLAND A, WAGNER J, et al. Interval partial least-squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy [J]. Appl Spectrosc,2000,54(3): 413-419.
[108]KIRKPATRICK S, GELATT JR C D, VECCHI M P. Optimization by simulated annealing [J]. Science,1983,220(4598):671-680.
[109]ROSSI F, FRAN OIS D, WERTZ V, et al. Fast selection of spectral variables with b-spline compression [J]. Chemometr Intell Lab,2007,86(2):208-218.
[110]LI H, LIANG Y, XU Q, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration [J]. Analytica chimica acta,2009,648(1):77-84.
[111]DE GROOT P, POSTMA G, MELSSEN W, et al. Influence of wavelength selection and data preprocessing on near-infrared-based classification of demolition waste [J]. Appl Spectrosc,2001,55(2):173-181.
[2]NOTI C, SEEBERGER P H. Chemical approaches to define the structure-activity relationship of heparin-like glycosaminoglycans [J]. Chem Biol, 2005,12(7):731-756.
[3]JIN L, ABRAHAMS J P, SKINNER R, et al. The anticoagulant activation of antithrombin by□ heparin [J]. Proc Natl Acad Sci USA,1997,94(26): 14683-14688.
[4]周先碗,何伟.酶降解法精制肝素钠[J].中国生物化学与分子生物学报,1999,15(3)：488-490.
[5]GREEN J P. Fractionation of Heparin on an Anion Exchanger [J]. Nature,1960, 186(4723):472.
[6]朱祖恒,丰树山.离子交换法精制肝素的研究[J].江苏化工,1998,26(1)：18-21.
[7]张万忠.肝素精制及低分子量肝素的制备[D].北京：北京化工大学生物工程系.2002.
[8]孙笛,袁佳.胡晓雁,等.一种基于近红外光谱的华蟾素醇沉过程指标成分及其转移率快速测定方法[J].中国中药杂志,2011,36(18)：2479-2483.
[9]VOLPI N. Fractionation of heparin, dermatan sulfate, and chondroitin sulfate by sequential precipitation:a method to purify a single glycosaminoglycan species from a mixture [J]. Anal Biochem,1994,218(2):382-391.
[10]VOLPI N. Purification of heparin, dermatan sulfate and chondroitin sulfate from mixtures by sequential precipitation with various organic solvents [J]. J Chromatogr B Biomed Sci Appl,1996,685(1):27-34.
[11]刘纯慧,奚涛,林亲雄,等.海胆黄多糖的分离,纯化及免疫活性测定[J].中国海洋药物杂志,2006,25(3)：7-11.
[12]蒋俊,罗珍,刘艳芳,等.醇沉条件对猴头菌多糖得率和品质的影响[J].食用菌学报,2011,18(2)：57-61.
[13]Guidance for Industty Heparin for Druy and Medical Device Use:Monitoring Crude Heparin for Quality [M]. FDA.2012.
[14]DOMANIG R, JOBSTL W, GRUBER S, et al. One-dimensional cellulose acetate plate electrophoresis--a feasible method for analysis of dermatan sulfate and other glycosaminoglycan impurities in pharmaceutical heparin [J]. J Pharm Biomed Anal,2009,49(1):151-155.
[15]VOLPI N, MACCARI F. Electrophoretic approaches to the analysis of complex polysaccharides [J]. J Chromatogr B Analyt Technol Biomed Life Sci,2006, 834(1-2):1-13.
[16]VOLPI N, MACCARI F, LINHARDT R J. Capillary electrophoresis of complex natural polysaccharides [J]. Electrophoresis,2008,29(15):3095-3106.
[17]NORWIG J, BEYER T, BRINZ D, et al. Prediction of the oversulphated chondroitin sulphate contamination of unfractionated heparin by ATR-IR spectrophotometry [J]. Pharmeur Sci Notes,2009(1):17-24.
[18]BIGLER P, BRENNEISEN R. Improved impurity fingerprinting of heparin by high resolution (1)H NMR spectroscopy [J]. J Pharm Biomed Anal,2009,49(4): 1060-1064.
[19]WALENGA J M, HOPPENSTEADT D A. Monitoring the new antithrombotic drugs [J]. Semin Thromb Hemost,2004.30(6):683-695.
[20]赵兴波,曹梦,张晓军.一种鉴定肝素制品动物物种来源的方法,CN101792804 A[P].2010.
[21]黄喜茹,王云志.用天青Ⅰ测定肝素钠注射液的效价[J].河北医学院学报，1994,15(3)：140-142.
[22]刘秀英,张超灿，徐卫林.甲苯胺蓝分光光度法测定肝素钠的研究[J].化学试剂,2009,31(4)：271-274.
[23]季钟煜,蒋传葵.肝素的简易化学测定法[J].生物化学与生物物理进展,1980,5：61-63.
[24]王磊,焦楠.羊血浆法测定海普林软膏中肝素钠生物效价的探讨[J].中国生化药物杂志,2002,23(3)：149-150.
[25]江燕,张力跃.山羊血浆代替绵羊血浆测定肝素钠效价[J].中国生化药物杂志,2000,21(5)：251.
[26]李京,范慧红.微量生色底物法测定肝素钠抗Ⅹa因子和抗Ⅱa因子活性[J].中国生化药物杂志,2010,31(1)：45-47.
[27]谢继青,于燕莉,黄元,等.应用分光光度法测定低分子肝素效价[M].中国药学会学术年会.2002.
[28]江燕,张力跃.粗品肝素钠质量情况和效价范围的判断[J].氨基酸和生物资源,2000,22(2)：16-18.
[29]江燕,张力跃.肝素钠生产中间产物效价的检测[J].中国医药工业杂志,2000,31(11)：494-496.
[30]蔡晨波.近红外基础研究：在线分析,多组份分析和空间效应[D].湖南大学,2008.
[31]ICH. ICH Harmonised tripartite guideline:Q8(R1) pharmaceutical development [M].2008.
[32]READ E K, PARK J T, SHAH R B, et al. Process analytical technology (PAT) for biopharmaceutical products:Part I. concepts and applications [J]. Biotechnol Bioeng,2010,105(2):276-284.
[33]RATHORE A, BHAMBURE R, GHARE V. Process analytical technology (PAT) for biopharmaceutical products [J]. Anal Bioanal Chem,2010,398(1):137-154.
[34]READ E, SHAH R, RILEY B, et al. Process analytical technology (PAT) for biopharmaceutical products:Part II. Concepts and applications [J]. Biotechnol Bioeng,2010,105(2):285-295.
[35]SEASHOLTZ M B. Making money with chemometrics [J]. Chemometr Intell Lab,1999,45(1-2):55-63.
[36]EL-HAGRASY A S, D'AMICO F, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending. Part I:D-optimal design for characterization of powder mixing and preliminary spectral data evaluation [J]. J Pharm Sci,2006,95(2):392-406.
[37]EL-HAGRASY A S, DELGADO-LOPEZ M, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending:Part Ⅱ:Qualitative near-infrared models for prediction of blend homogeneity [J]. J Pharm Sci,2006,95(2):407-421.
[38]EL-HAGRASY A S, DRENNEN J K. A process analytical technology approach to near-infrared process control of pharmaceutical powder blending. Part III: Quantitative near-infrared calibration for prediction of blend homogeneity and characterization of powder mixing kinetics [J]. J Pharm Sci,2006,95(2): 422-444.
[39]MARK J, KARNER M, ANDRE M, et al. Online process control of a pharmaceutical intermediate in a fluidized-bed drier environment using near-infrared spectroscopy [J]. Anal Chem,2010,82(10):4209-4215.
[40]HALSTENSEN M, DE BAKKER P, ESBENSEN K H. Acoustic chemometric monitoring of an industrial granulation production process—a PAT feasibility study [J]. Chemometr Intell Lab,2006,84(1-2):88-97.
[41]KIRSCH J D, DRENNEN J K. Determination of film-coated tablet parameters by near-infrared spectroscopy [J]. J Pharm Biomed Anal,1995,13(10): 1273-1281.
[42]CHAN C-M, LO W, WONG K-Y. Application of a luminescence-based pH optrode to monitoring of fermentation by Klebsiella pneumoniae [J]. Biosens Bioelectron,2000,15(1-2):7-11.
[43]CHEN Y, KROL J, HUANG W, et al. DCO2 on-line measurement used in rapamycin fed-batch fermentation process [J]. Process Biochem 2008,43(4): 351-355.
[44]HEINZLE E, OEGGERLI A, DETTWILER B. On-line fermentation gas analysis:Error analysis and application of mass spectrometry [J]. Analytica Chimica Acta,1990.238(0):101-115.
[45]HUN-CHI S, H KANSON H, MATTIASSON B. On-line monitoring of D-lactic acid during a fermentation process using immobilized D-lactate dehydrogenase in a sequential injection analysis system [J]. Analytica Chimica Acta.1995. 300(1-3):277-285.
[46]ZHANG J, ZHOU H, JI Z, et al. Monoclonal antibody production with on-line harvesting and process monitoring [J]. J Chromatogr B Biomed Sci Appl,1998. 707(1-2):257-265.
[47]BRAD S. Process analytical technology:A strategy for keeping manufacturing viable in Australia [J]. Vib Spectrosc,2007,44(1):171-178.
[48]ROGGO Y, CHALUS P. MAURER L, et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies [J]. J Pharm Biomed Anal,2007,44(3):683-700.
[49]褚小立.化学计量学方法与分子光谱分析技术[M].北京：化学工业出版社.2011.
[50]BLANCO M, COELLO J, ITURRIAGA H, et al. Near-infrared spectroscopy in the pharmaceutical industry. Critical Review [J]. Analyst,1998,123(8): 135R-150R.
[51]VAN DER VLIES C. The use of NIR spectrometry in the pharmaceutical QC laboratory [J]. Eur J Pharm Sci,1994,2(1-2):79-81.
[52]EBUBE N K, THOSAR S S, ROBERTS R A, et al. Application of near-infrared spectroscopy for nondestructive analysis of Avicel powders and tablets [J]. Pharm Dev Technol,1999,4(1):19-26.
[53]KREFT K, KOZAMERNIK B, URLEB U. Qualitative determination of polyvinylpyrrolidone type by near-infrared spectrometry [J]. Int J Pharm,1999, 177(1):1-6.
[54]LI YOON W, D. JEE R, C. MOFFAT A, et al. Construction and transferability of a spectral library for the identification of common solvents by near-infrared transflectance spectroscopy [J]. Analyst,1999,124(8):1197-203.
[55]DE MAESSCHALCK R, VAN DEN KERKHOF T. Implementation of a simple semi-quantitative near-infrared method for the classification of clinical trial tablets [J]. J Pharm Biomed Anal,2005,37(1):109-114.
[56]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.
[57]LEE H E, LEE M J, KIM W S, et al. In-line monitoring and interpretation of an indomethacin anti-solvent crystallization process by near-infrared spectroscopy (NIRS) [J]. Int J Pharm,2011,420(2):274-281.
[58]MORISSEAU K M, RHODES C T. Near-infrared spectroscopy as a nondestructive alternative to conventional tablet hardness testing [J]. Pharm Res, 1997,14(1):108-111.
[59]KIRSCH J D, DRENNEN J K. Nondestructive tablet hardness testing by near-infrared spectroscopy:a new and robust spectral best-fit algorithm [J]. J Pharm Biomed Anal,1999,19(3-4):351-362.
[60]BLANCO M, ALCAL M. Content uniformity and tablet hardness testing of intact pharmaceutical tablets by near infrared spectroscopy:A contribution to process analytical technologies [J]. Analytica Chimica Acta,2006,557(1-2): 353-359.
[61]BLANCO M, ALCAL M, GONZ LEZ J M, et al. A process analytical technology approach based on near infrared spectroscopy:Tablet hardness, content uniformity, and dissolution test measurements of intact tablets [J]. J Pharm Sci,2006,95(10):2137-2144.
[62]HATTORI Y, OTSUKA M. NIR spectroscopic study of the dissolution process in pharmaceutical tablets [J]. Vib Spectrosc,2011,57:275-281.
[63]BERNTSSON O, BURGER T, FOLESTAD S, et al. Effective sample size in diffuse reflectance near-IR spectrometry [J]. Anal Chem,1999,71(3):617-623.
[64]RANTANEN J, ANTIKAINEN O, MANNERMAA J P, et al. Use of the near-infrared reflectance method for measurement of moisture content during granulation [J]. Pharm Dev Technol,2000,5(2):209-217.
[65]ZHENG Y, LAI X, BRUUN S W, et al. Determination of moisture content of lyophilized allergen vaccines by NIR spectroscopy [J]. J Pharm Biomed Anal, 2008,46(3):592-596.
[66]LI Y, FAN Q, LIU S, et al. Simultaneous analysis of moisture, active component and cake structure of lyophilized powder for injection with diffuse reflectance FT-NIR chemometrics [J]. J Pharm Biomed Anal,2011,55(1):216-219.
[67]SARRAGUCA M C, SOARES S O, LOPES J A. A near-infrared spectroscopy method to determine aminoglycosides in pharmaceutical formulations [J]. Vib Spectrosc,2011,56:184-192.
[68]LIU Y Y, HU C Q. Construction of predictive models for gentamicin contents in aqueous solution using specific near infrared spectral regions [J]. Vibrational Spectroscopy,2010,55(2):241-249.
[69]DONG Q, ZANG H C, LIU A H, et al. Determination of molecular weight of hyaluronic acid by near-infrared spectroscopy [J]. J Pharm Biomed Anal,2010, 53:274-278.
[70]SUN C X, ZANG H C, LIU X M, et al. Determination of potency of heparin active pharmaceutical ingredient by near infrared spectroscopy [J]. J Pharm Biomed Anal,2010,51:1060-1063.
[71]HUANG H, QU H. In-line monitoring of alcohol precipitation by near-infrared spectroscopy in conjunction with multivariate batch modeling [J]. Analytica Chimica Acta,2011,707(1-2):47-56.
[72]WARGO D J, DRENNEN J K. Near-infrared spectroscopic characterization of pharmaceutical powder blends [J]. J Pharm Biomed Anal,1996,14(11): 1415-1423.
[73]COGDILL R, ANDERSON C, DELGADO-LOPEZ M, et al. Process analytical technology case study part Ⅰ:Feasibility studies for quantitative near-infrared method development [J]. AAPS PharmSciTech,2005,6(2):E262-E272.
[74]COGDILL R, ANDERSON C, DELGADO M, et al. Process analytical technology case study:Part Ⅱ. Development and validation of quantitative near-infrared calibrations in support of a process analytical technology application for real-time release [J]. AAPS PharmSciTech,2005,6(2): E273-E283.
[75]COGDILL R, ANDERSON C, DRENNEN J. Process analytical technology case study, part III:Calibration monitoring and transfer [J]. AAPS PharmSciTech, 2005,6(2):E284-E297.
[76]CHABLANI L, TAYLOR M K, MEHROTRA A, et al. Inline real-time nearinfrared granule moisture measurements of a continuous granulation-drying-milling process [J]. AAPS PharmSciTech,2011, 12(4):1050-1055.
[77]DE BEER T, VERCRUYSSE P, BURGGRAEVE A, et al. In-line and real time process monitoring of a freeze drying process using Raman and NIR spectroscopy as complementary process analytical technology (PAT) tools [J]. J Pharm Sci,2009,98(9):3430-3446.
[78]LI W, WOROSILA G D, WANG W, et al. Determination of polymorph conversion of an active pharmaceutical ingredient in wet granulation using NIR calibration models generated from the premix blends [J]. J Pharm Sci,2005, 94(12):2800-2806.
[79]MOLTGEN C V, PUCHERT T, MENEZES J C, et al. A novel in-line NIR spectroscopy application for the monitoring of tablet film coating in an industrial scale process [J]. Talanta,2012,92:26-37.
[80]LEE M J, SEO D Y, LEE H E, et al. In line NIR quantification of film thickness on pharmaceutical pellets during a fluid bed coating process [J]. Int J Pharm, 2011,403(1-2):66-72.
[81]XIE L-G, SUN H-M, JIN S-H. Screening adulteration of polypropylene bottles with postconsumer recycled plastics for oral drug package by near-infrared spectroscopy [J]. Analytica Chimica Acta,2011,706(2):312-320.
[82]CAVINATO A G, MA YES D M, GE Z H, et al. Noninvasive method for monitoring ethanol in fermentation processes using fiber-optic near-infrared spectroscopy [J]. Anal Chem,1990,62(18):1977-1982.
[83]MORITA H, HASUNUMA T, VASSILEVA M, et al. Near infrared spectroscopy as high-throughput technology for screening of xylose-fermenting recombinant Saccharomyces cerevisiae strains [J]. Anal Chem,2011,83(11): 4023-4029.
[84]LIC NIA O. RODRIGUESB L V, JOAQUIM P. CARDOSOA, JOS C. MENEZES. The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems [J]. Talanta,2008,75(5(15)):1356-1361
[85]ARNOLD S A, CROWLEY J, WOODS N, et al. In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation [J]. Biotechnol Bioeng,2003,84(1):13-19.
[86]PIETERS S, DE BEER T, KASPER J C, et al. Near-infrared spectroscopy for in-Line monitoring of protein unfolding and its interactions with lyoprotectants during freeze-drying [J]. Ana Chem,2011,84(2):947-955.
[87]SCARFF M, ARNOLD S A, HARVEY L M, et al. Near infrared spectroscopy for bioprocess monitoring and control:current status and future trends [J]. Crit Rev Biotechnol,2006,26(1):17-39.
[88]BURNS D A, CIURCZAK E W. Handbook of near-infrared analysis[M]. CRC Press.2008.
[89]BRIDGES C C. Hierarchical cluster analysis [J]. Psychol Rep,1966,18(3): 851-854.
[90]FOOT M, MULHOLLAND M. Classification of chondroitin sulfate A, chondroitin sulfate C, glucosamine hydrochloride and glucosamine 6 sulfate using chemometric techniques [J]. J Pharm Biomed Anal,2005,38(3):397-407.
[91]MASSART D L. Chemometrics:a textbook [M]. Elsevier Science,1988.
[92]MARK H L, TUNNELL D. Qualitative near-infrared reflectance analysis using Mahalanobis distances [J]. Anal Chem 1985,57(7):1449-1456.
[93]SHARAF M A, ILLMAN D L. KOWALSKI B R. Chemometrics [M]. Wiley-Interscience,1986.
[94]DERDE M P, BUYDENS L, GUNS C, et al. Comparison of rule-building expert systems with pattern recognition for the classification of analytical data [J]. Anal Chem,1987,59(14):1868-1671.
[95]WOLD S. Pattern recognition by means of disjoint principal components models [J]. Pattern Recognition,1976,8(3):127-139.
[96]ST HLE L, WOLD S. Partial least squares analysis with cross-validation for the two-class problem:A Monte Carlo study [J]. J Chemometr,1987,1(3):185-196.
[97]LEARDI R. Nature-inspired methods in chemometrics:genetic algorithms and artificial neural networks [M]. Elsevier Science,2003.
[98]ZANG H, LI L, WANG F, et al. A method for identifying the origin of chondroitin sulfate with near infrared spectroscopy [J]. J Pharm Biomed Anal, 2012,61:224-229.
[99]N S T, ISAKSSON T, FEARN T, et al. A user friendly guide to multivariate calibration and classification [M]. NIR Publications,2002.
[100]ADAMS M J. Chemometrics in analytical spectroscopy [M]. Springer Verlag, 2004.
[101]BALABIN R M, LOMAKINA E I, SAFIEVA R Z. Neural network (ANN) approach to biodiesel analysis:Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy [J]. Fuel, 2011,90(5):2007-2015.
[102]BALABIN R M, LOMAKINA E I. Support vector machine regression (SVR/LS-SVM)-an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data [J]. Analyst,2011,136(8):1703-1712.
[103]PONTES M J C, GALV O R K H, ARA JO M C U, et al. The successive projections algorithm for spectral variable selection in classification problems [J]. Chemometr Intell Lab,2005,78(1-2):11-18.
[104]ARA JO M C U, SALDANHA T C B, GALV O R K H,et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis [J]. Chemometr Intell Lab,2001,57(2):65-73.
[105]CENTNER V, MASSART D-L, DE NOORD O E, et al. Elimination of uninformative variables for multivariate calibration [J]. Anal Chem,1996,68(21): 3851-3858.
[106]LEARDI R, LUPI EZ GONZ LEZ A. Genetic algorithms applied to feature selection in PLS regression:how and when to use them [J]. Chemometr Intell Lab,1998,41(2):195-207.
[107]NORGAARD L, SAUDLAND A, WAGNER J, et al. Interval partial least-squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy [J]. Appl Spectrosc,2000,54(3): 413-419.
[108]KIRKPATRICK S, GELATT JR C D, VECCHI M P. Optimization by simulated annealing [J]. Science,1983,220(4598):671-680.
[109]ROSSI F, FRAN OIS D, WERTZ V, et al. Fast selection of spectral variables with b-spline compression [J]. Chemometr Intell Lab,2007,86(2):208-218.
[110]LI H, LIANG Y, XU Q, et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration [J]. Analytica chimica acta,2009,648(1):77-84.
[111]DE GROOT P, POSTMA G, MELSSEN W, et al. Influence of wavelength selection and data preprocessing on near-infrared-based classification of demolition waste [J]. Appl Spectrosc,2001,55(2):173-181.