近红外光谱技术在药物检测中的应用研究
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摘要
本文研究了近红外光谱分析技术在药物成分分析、中草药定性定量检测以及制药过程监控方面的应用。着重对盐酸左氧氟沙星、甲磺酸培氟沙星、盐酸西替利嗪、淫羊藿等4种药物进行了近红外光谱图信息提取和定量分析工作,探讨了不同预处理方法对模型的影响,介绍了近红外光谱技术在肺宁颗粒在线检测过程中的应用。
对盐酸左氧氟沙星进行近红外光谱的检测,比较了PLS法和ANN法对盐酸左氧氟沙星针剂建立的定量模型。仿真实验表明,PLS法显示了更加优越的性能,应用此法可直接对大量未知样品进行测定。
对甲磺酸培氟沙星进行近红外光谱的检测,利用PLS法对甲磺酸培氟沙星建立了定量模型。仿真实验表明,PLS法建立的模型预测结果准确。利用PCR法对甲磺酸培氟沙星进行了定量建模,比较了三种不同预处理方法,结果显示经过归一化处理后的模型效果最好。
将近红外漫反射技术应用到盐酸西替利嗪的定量检测中,详细比较了PLS法和PCR法对模型的影响。经比较,利用PLS方法来处理盐酸西替利嗪片剂表现出更加优越的性能,样品的化学测定值和模型预测值的相关性更好。
将近红外光谱技术与化学计量学方法联用,对淫羊藿进行了产地鉴别,同时实现对其主成分的定量检测。结果表明此方法可简化测试步骤,缩短检测时间,节约测试成本,为近红外光谱技术应用于淫羊藿等中草药的定性、定量检测提供了理论依据。
介绍了近红外光谱技术在中药生产过程控制中的应用,概述了肺宁颗粒的制备过程和在线检测系统,分析了近红外光谱在线检测技术的前景及存在的问题。
The active ingredient (AI) of a drug is not only its essential component, but also the most important determinant of the drug potency. The quality and quantity of the AI in drugs are directly related to their medicinal properties. Therefore, the quality and quantity of AI in drugs are very important markers of their effectiveness, which must be put under strictly control during the manufacture and sale process of drugs. Instead of analyzing the final drug products statically, the analysts must also see to the detailed qualities of the raw material, the intermediate products, the semi-finished products and the final products, in order to realize a real-time, on-site and in-line quality control. In this study, we developed a new method for quick, simple and accurate analysis of some drugs.
Near infrared (NIR) normally refer to the electromagnetic wave which has a wavelength between 780nm to 2526nm. Generally, the near infrared region can be further sub-divided into two regions: the 780nm– 1100nm region, which is called short-wavelength near infrared, and the 1100nm– 2526nm region, which is called long-wavelength near infrared. Spectroscopic signals in near infrared region are generated by the single, double and triple bond signals of the absorption frequencies of O-H, C-H and N-H chemical bonds. Since this region is crowded with the double and triple bonds signals, and with severe overlap of signals, it is generally to be considered as a weak informative region, which means it can not be analyzed in a routine manner. Modern near infrared spectroscopic technology however, combines the power of chemometrics and infrared spectroscopy. It makes use of the power of quick and simple data collection of IR spectroscopy and the information extraction and processing power of chemometrics. The key technical point of recent near infrared technology is to set up the multi-factor correction model of infrared data. With the development of the computer science and applied mathematics and their applications in analytical chemistry, NIR spectroscopy and chemometrics are getting more and more attention since 1980s. It has been introduced into variable research fields and has been developed into an independent analytical technique, and changing the old idea that the theoretical study are lagging behind the practical experience in analytical chemistry.
In present study, we used modern NIR spectroscopy to analyze the active ingredient of drugs. Modern NIR spectroscopy uses the transmitted and reflective spectra to analyze the chemical contents and the molecular structures of certain materials. It has several advantages, such as no contamination to the samples, no contact detection, non-destructive, easy to handle and easy to be integrated into the production line. Any substance would generate absorption signal in NIR region, which contains plenty of structural and molecular information. Thus the NIR spectroscopy can be used into multiple fields such as agriculture, medicine and health science. For instance, in the field of medicinal analytical study, NIR spectroscopy has been applied to analyze not only different dosage forms such as raw materials, tablet, capsule and liquid, but also different types of drugs, such as, protein, herbal medicine and antibiotics. NIR is even more useful to analyze the purity of the raw drug materials, packaging materials, and to monitor the production technique; different optical fiber probes can be utilized to analyze and control the production process. The key point of the modern NIR spectroscopy is how to extract useful information from the spectra.
In present study we emphasize the method of extracting useful information from the NIR spectra during the analysis of drugs. We use the spectra of several synthetic and herbal medicines as examples to investigate the data processing of NIR spectra using wavelet transform, partial least square method (PLS), artificial neural network (ANN) and other chemometric methods.
We use NIR spectroscopic methods to perform quantitative analysis for several synthetic and herbal medicines and compare the results with conventional methods accordingly, thus to prove the effectiveness and accuracy of the NIR spectroscopic analysis method.
1.We used Levofloxacin Hemihydrate as the first target of our study. We use NIR combined with PLS or ANN methods to rapidly determine its amount. We set up the quantitative analysis models for PLS or ANN methods, compared the modeling process, the optimization of the parameters and the accuracy of the results for these two methods, and we discussed the optimized methods for setting up the important parameters in these two models in detail. The R2 and RMSEP for PLS model were 0.964 and 0.2428, respectively. Whereas for the ANN model, the R2 and RMSEP after wavelet transform was 0.944 and 0.5722, respectively. Virtual experiment suggested that, both of these two methods result in accurate models with can give good prediction. However, the PLS model is a little bit better than ANN model, indicated by its less error and deviation. The prediction of PLS model also has better correlation with the results of chemical assay.
2.NIR spectroscopy was also used to analyze the pefloxacin mesilate (PM) injection solution. The recorded NIR spectra were analyzed and the quantitative model was generated by partial least square method and principle component regression (PCR) method. The R2 value, RMSEP and RPD of the PLS model, generated from several different batches of PM injection solution, were 0.959, 0.00087 and 4.95. The virtual experiment based on the different models suggested that the model generated using partial least square method gave us accurate prediction. The performance of this model was good, which showed small mean square deviation and mean error. For the model generated by PCR method, three different preliminary data processing methods, e.g. smoothing, normalization and multivariate scatter-correction, were used and the results were compared in detail. The normalization gave us the best results.
3.Similar approach was used to perform the quantitative analysis for Cetirizine Hydrochloride samples. PLS and PCR methods were used to generate the quantitative models. The modeling process, optimization of the parameters and the accuracy of the results using these two methods were compared. PLS method with cross-validation, first-order derivative and vector normalization data processing, was used to generate the quantitative analysis model. The resulted model had aR2 value of 0.878, the RMSECV value was 5.49E-5 and RPD value was 2.55. For the model generated by PCR method, different data processing methods, i.e. smoothing, normalization, were used and compared, multivariate scatter-correction, noise suppression, derivative, baseline correction and selection of variables, were used and compared. It was found that the normalization method gave us the best results, which has a correlation coefficient of 0.81, root mean square deviation of 0.000451 and mean residual error of -1.32E-05. Compare the result of these two methods, both PLS model and PCR model shows good generalization capability and accuracy of prediction. However, the PLS methods shows a little bit better performance while dealing with the Cetirizine Hydrochloride tablet samples ,with considerable less relative error and root mean square deviation than PCR methods, its prediction also has a better correlation coefficient with the experimental assay results. These proved that the PLS could be used to handle large amount of samples.
4.NIR spectroscopy and chemometrics were also used to identify the origins of Aceranthus sagittatus samples and to quantitatively analyze their active ingredient content. Nine different samples collected from different areas were analyzed, using vector normalization and/or first–order derivative processing methods. The results suggest that the vector normalization data processing gave us better qualitative identification model, the Cluster analysis gave us satisfactory results. The classification error rate was 0. It can effectively identify the different origins of the samples thus this resulted qualitative analysis model can be used to quickly identify the origin of unknown samples. This analysis method is fast, simple and low-cost, which provide us a novel way for the qualitative identification of herbal medicine. The active ingredient of these Aceranthus sagittatus samples were determined using HPLC method, the results were used as the training data to generate the NIR quantitative analysis model using PLS methods. After multiple optimized preliminary data processing procedure, the wavelength range for the further study was chosen as 7502.1～4597.7 cm-1. 42 different Aceranthus sagittatus samples were used as the calibration data set. After multivariate scatter-correction, the model gave us satisfactory results. The optimized model was used to predict the active ingredient of 9 random Aceranthus sagittatus samples. The results indicated that the standard deviation of the prediction was 0.0206, which means that it has very good accuracy of prediction and very effective.
5.In present study, we also discussed some of the application of modern NIR spectroscopic technology in the in-line control of herbal medicine production. We reviewed the manufacture process of the product“fei ning ke li”and its in-line production control system, especially the effect of raw material pretreatment system, the structure and analysis model of the NIR spectrometer in this system and how this system has been established. We also discussed the perspective and potential problems of the application of NIR technique in in-line diction system.
This study provided a deep insight into the information extraction methods and quantitative analysis of NIR spectra, and compared the effects of different chemometric methods for the data processing. It also acquired valuable spectroscopic character of these several drugs in near infrared region. , which will help to establish the spectroscopic database for the analysis of these drugs, and provided a solid foundation for the further development of the in-line production control system. The experimental data also indicated that this proposed NIR data analysis method increased the accuracy of the analysis, which has a high practical value. This proposed quantitative analysis method was quick and simple, low-cost, without the requirement of complicated sample pretreatment. It can be easily applied to analyze large amount of sample. This study provided an effective method for pharmaceutical analysis, and also provided an convincing evidence to support the application of NIR spectroscopy in the analysis of drugs.
对盐酸左氧氟沙星进行近红外光谱的检测,比较了PLS法和ANN法对盐酸左氧氟沙星针剂建立的定量模型。仿真实验表明,PLS法显示了更加优越的性能,应用此法可直接对大量未知样品进行测定。
对甲磺酸培氟沙星进行近红外光谱的检测,利用PLS法对甲磺酸培氟沙星建立了定量模型。仿真实验表明,PLS法建立的模型预测结果准确。利用PCR法对甲磺酸培氟沙星进行了定量建模,比较了三种不同预处理方法,结果显示经过归一化处理后的模型效果最好。
将近红外漫反射技术应用到盐酸西替利嗪的定量检测中,详细比较了PLS法和PCR法对模型的影响。经比较,利用PLS方法来处理盐酸西替利嗪片剂表现出更加优越的性能,样品的化学测定值和模型预测值的相关性更好。
将近红外光谱技术与化学计量学方法联用,对淫羊藿进行了产地鉴别,同时实现对其主成分的定量检测。结果表明此方法可简化测试步骤,缩短检测时间,节约测试成本,为近红外光谱技术应用于淫羊藿等中草药的定性、定量检测提供了理论依据。
介绍了近红外光谱技术在中药生产过程控制中的应用,概述了肺宁颗粒的制备过程和在线检测系统,分析了近红外光谱在线检测技术的前景及存在的问题。
The active ingredient (AI) of a drug is not only its essential component, but also the most important determinant of the drug potency. The quality and quantity of the AI in drugs are directly related to their medicinal properties. Therefore, the quality and quantity of AI in drugs are very important markers of their effectiveness, which must be put under strictly control during the manufacture and sale process of drugs. Instead of analyzing the final drug products statically, the analysts must also see to the detailed qualities of the raw material, the intermediate products, the semi-finished products and the final products, in order to realize a real-time, on-site and in-line quality control. In this study, we developed a new method for quick, simple and accurate analysis of some drugs.
Near infrared (NIR) normally refer to the electromagnetic wave which has a wavelength between 780nm to 2526nm. Generally, the near infrared region can be further sub-divided into two regions: the 780nm– 1100nm region, which is called short-wavelength near infrared, and the 1100nm– 2526nm region, which is called long-wavelength near infrared. Spectroscopic signals in near infrared region are generated by the single, double and triple bond signals of the absorption frequencies of O-H, C-H and N-H chemical bonds. Since this region is crowded with the double and triple bonds signals, and with severe overlap of signals, it is generally to be considered as a weak informative region, which means it can not be analyzed in a routine manner. Modern near infrared spectroscopic technology however, combines the power of chemometrics and infrared spectroscopy. It makes use of the power of quick and simple data collection of IR spectroscopy and the information extraction and processing power of chemometrics. The key technical point of recent near infrared technology is to set up the multi-factor correction model of infrared data. With the development of the computer science and applied mathematics and their applications in analytical chemistry, NIR spectroscopy and chemometrics are getting more and more attention since 1980s. It has been introduced into variable research fields and has been developed into an independent analytical technique, and changing the old idea that the theoretical study are lagging behind the practical experience in analytical chemistry.
In present study, we used modern NIR spectroscopy to analyze the active ingredient of drugs. Modern NIR spectroscopy uses the transmitted and reflective spectra to analyze the chemical contents and the molecular structures of certain materials. It has several advantages, such as no contamination to the samples, no contact detection, non-destructive, easy to handle and easy to be integrated into the production line. Any substance would generate absorption signal in NIR region, which contains plenty of structural and molecular information. Thus the NIR spectroscopy can be used into multiple fields such as agriculture, medicine and health science. For instance, in the field of medicinal analytical study, NIR spectroscopy has been applied to analyze not only different dosage forms such as raw materials, tablet, capsule and liquid, but also different types of drugs, such as, protein, herbal medicine and antibiotics. NIR is even more useful to analyze the purity of the raw drug materials, packaging materials, and to monitor the production technique; different optical fiber probes can be utilized to analyze and control the production process. The key point of the modern NIR spectroscopy is how to extract useful information from the spectra.
In present study we emphasize the method of extracting useful information from the NIR spectra during the analysis of drugs. We use the spectra of several synthetic and herbal medicines as examples to investigate the data processing of NIR spectra using wavelet transform, partial least square method (PLS), artificial neural network (ANN) and other chemometric methods.
We use NIR spectroscopic methods to perform quantitative analysis for several synthetic and herbal medicines and compare the results with conventional methods accordingly, thus to prove the effectiveness and accuracy of the NIR spectroscopic analysis method.
1.We used Levofloxacin Hemihydrate as the first target of our study. We use NIR combined with PLS or ANN methods to rapidly determine its amount. We set up the quantitative analysis models for PLS or ANN methods, compared the modeling process, the optimization of the parameters and the accuracy of the results for these two methods, and we discussed the optimized methods for setting up the important parameters in these two models in detail. The R2 and RMSEP for PLS model were 0.964 and 0.2428, respectively. Whereas for the ANN model, the R2 and RMSEP after wavelet transform was 0.944 and 0.5722, respectively. Virtual experiment suggested that, both of these two methods result in accurate models with can give good prediction. However, the PLS model is a little bit better than ANN model, indicated by its less error and deviation. The prediction of PLS model also has better correlation with the results of chemical assay.
2.NIR spectroscopy was also used to analyze the pefloxacin mesilate (PM) injection solution. The recorded NIR spectra were analyzed and the quantitative model was generated by partial least square method and principle component regression (PCR) method. The R2 value, RMSEP and RPD of the PLS model, generated from several different batches of PM injection solution, were 0.959, 0.00087 and 4.95. The virtual experiment based on the different models suggested that the model generated using partial least square method gave us accurate prediction. The performance of this model was good, which showed small mean square deviation and mean error. For the model generated by PCR method, three different preliminary data processing methods, e.g. smoothing, normalization and multivariate scatter-correction, were used and the results were compared in detail. The normalization gave us the best results.
3.Similar approach was used to perform the quantitative analysis for Cetirizine Hydrochloride samples. PLS and PCR methods were used to generate the quantitative models. The modeling process, optimization of the parameters and the accuracy of the results using these two methods were compared. PLS method with cross-validation, first-order derivative and vector normalization data processing, was used to generate the quantitative analysis model. The resulted model had aR2 value of 0.878, the RMSECV value was 5.49E-5 and RPD value was 2.55. For the model generated by PCR method, different data processing methods, i.e. smoothing, normalization, were used and compared, multivariate scatter-correction, noise suppression, derivative, baseline correction and selection of variables, were used and compared. It was found that the normalization method gave us the best results, which has a correlation coefficient of 0.81, root mean square deviation of 0.000451 and mean residual error of -1.32E-05. Compare the result of these two methods, both PLS model and PCR model shows good generalization capability and accuracy of prediction. However, the PLS methods shows a little bit better performance while dealing with the Cetirizine Hydrochloride tablet samples ,with considerable less relative error and root mean square deviation than PCR methods, its prediction also has a better correlation coefficient with the experimental assay results. These proved that the PLS could be used to handle large amount of samples.
4.NIR spectroscopy and chemometrics were also used to identify the origins of Aceranthus sagittatus samples and to quantitatively analyze their active ingredient content. Nine different samples collected from different areas were analyzed, using vector normalization and/or first–order derivative processing methods. The results suggest that the vector normalization data processing gave us better qualitative identification model, the Cluster analysis gave us satisfactory results. The classification error rate was 0. It can effectively identify the different origins of the samples thus this resulted qualitative analysis model can be used to quickly identify the origin of unknown samples. This analysis method is fast, simple and low-cost, which provide us a novel way for the qualitative identification of herbal medicine. The active ingredient of these Aceranthus sagittatus samples were determined using HPLC method, the results were used as the training data to generate the NIR quantitative analysis model using PLS methods. After multiple optimized preliminary data processing procedure, the wavelength range for the further study was chosen as 7502.1～4597.7 cm-1. 42 different Aceranthus sagittatus samples were used as the calibration data set. After multivariate scatter-correction, the model gave us satisfactory results. The optimized model was used to predict the active ingredient of 9 random Aceranthus sagittatus samples. The results indicated that the standard deviation of the prediction was 0.0206, which means that it has very good accuracy of prediction and very effective.
5.In present study, we also discussed some of the application of modern NIR spectroscopic technology in the in-line control of herbal medicine production. We reviewed the manufacture process of the product“fei ning ke li”and its in-line production control system, especially the effect of raw material pretreatment system, the structure and analysis model of the NIR spectrometer in this system and how this system has been established. We also discussed the perspective and potential problems of the application of NIR technique in in-line diction system.
This study provided a deep insight into the information extraction methods and quantitative analysis of NIR spectra, and compared the effects of different chemometric methods for the data processing. It also acquired valuable spectroscopic character of these several drugs in near infrared region. , which will help to establish the spectroscopic database for the analysis of these drugs, and provided a solid foundation for the further development of the in-line production control system. The experimental data also indicated that this proposed NIR data analysis method increased the accuracy of the analysis, which has a high practical value. This proposed quantitative analysis method was quick and simple, low-cost, without the requirement of complicated sample pretreatment. It can be easily applied to analyze large amount of sample. This study provided an effective method for pharmaceutical analysis, and also provided an convincing evidence to support the application of NIR spectroscopy in the analysis of drugs.
引文
[1] Blanco M., Villarroya I. NIR spectroscopy: a rapid-response analytical tool. TRAC-TRENDS IN ANALYTICAL CHEMISTRY. 2002. 21(4). 240-250
[2]陆婉珍.现代近红外光谱分析技术.中国石化出版社. 2000. 260
[3] Davies T. The history of near infrared spectroscopic analysis: Past, present and future–“From sleeping technique to the morning star of spectroscopy”. ANALUSIS MAGAZINE. 1998. 26(4). 17-19
[4]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.北京中国轻工业出版社. 2005. 113
[5] Lunardelli M., Marchi G., Martiis F., Application of near infrared reflectance spectroscopy (NIRS) to process control in the pharmaceutical industry. BOLLETTINO CHIMICO FARMACEUTICO. 127(1). 13-19
[6] Blanco M., Romero M. A. Near-infrared libraries in the pharmaceutical industry: a solution for identity confirmation. ANALYST. 2001. 126(12). 2212-2217
[7] Blanco M., Coello J., Eustaquio A., Iturriaga H., Maspoch S. Development and validation of a method for the analysis of a pharmaceutical preparation by near-infrared diffuse reflectance spectroscopy. JOURNAL OF PHARMACEUTICAL SCIENCES. 1999. 88(5). 551-556
[8] Blanco M., Villar A. Development and validation of a method for the polymorphic analysis of pharmaceutical preparations using near infrared spectroscopy. JOURNAL OF PHARMACEUTICAL SCIENCES. 2003. 92(4). 823-830
[9] Dreassi E., Corti G., Ceramelli P. Near-infrared reflectance spectrometry in the determination of the physical state of primary materials in pharmaceutical production. ANALYST. 1995. 120. 1005-1008
[10] Kramer K., Ebel S. Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients. ANALYTICA CHIMICA ACTA. 2000. 420(2). 155-161
[11] Dempster M. A., Jones J.A., Last I.R. Near-infrared methods for the identification of tablets in clinical trial supplies. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 1993. 11(11-12). 1087-1092
[12] Scafi S.H.F., Pasquini C. Identification of counterfeit drugs using near-infrared spectroscopy. ANALYST. 2001. 126(12). 2218-2224
[13] Corti P., Dreassi E., Murratzu C., Corbini G., Ballerini L., Gravina S. Application of NIRS to the quantity control of pharmaceuticals ketoprofen assay in different pharmaceutical formulae. PHARMACEUTICA ACTA HELVETIAe. 1989. 64(5-6). 140-145
[14] Tudor A. M., Church S. J., Hendra P. J., Davies M. C., Melia C. D. Qualitative and quantitative analysis of chlorpropamide polymorphic mixtures by near infrared fourier transform raman spectroscopy. PHARMACEUTICAL RESEARCH. 1993. 10(12). 1772-1776
[15] Woo Y. A., Kim H. J., Cho J., Chung H. Discrimination of herbal medicines according to geographical origin with near infrared reflectance spectroscopy and pattern recognition techniques. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 1999. 21(2). 407-413
[16] Woo Y. A., Cho C. H., Kim H. J., Yang J. S., Seong K. Y. Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES. MICROCHEMICAL JOURNAL. 2002. 73(3). 299-306
[17] Laasonen M., Harmia-Pulkkinen T., Simard C. L., Michiels E., Rasanen M., Vuorela H. Fast identification of Echinacea purpurea dried roots using near-infrared spectroscopy. ANALYTICAL CHEMISTRY. 2002. 74(11). 2493-2499
[18] Chen Y., S?rensen L. K. Determination of marker constituents in radix Glycyrrhizae and radix Notoginseng by near infrared spectroscopy. FRESENIUS' JOURNAL OF ANALYTICAL CHEMISTRY. 367(5). 491-496
[19] Ikeda-Dantsuji Y., Konomi I., Nagayama A. Effects of levofloxacin and doxycycline on interleukin-6 production of Chlamydia trachomatis-infected human synovial fibroblasts. CHEMOTHERAPY. 2007. 53(5). 332-337
[20] Argyropoulos A. N., Doumas K., Farmakis A., Liakatas I., Gkialas I., Lykourinas M. Time of administration of a single dose of oral levofloxacin and its effect in infectious complications from transrectal prostate biopsy. INTERNATIONAL UROLOGY AND NEPHROLOGY. 2007. 39(3). 897-903
[21] Kelesidis T., Canseco E. Levofloxacin-induced Hypoglycemia: A Rare but Life-threatening Side Effect of a Widely Used Antibiotic. AMERICAN JOURNAL OF MEDICINE. 2009. 122(3). E3-E4
[22] Anderson V. R., Perry C. M. Levofloxacin - A review of its use as a high-dose, short-course treatment for bacterial infection. DRUGS. 2008. 68(4). 535-565
[23] Ferrer C., Rodriguez A., Abad J. L., Fernandez J., Alio J. L. Bactericidal effect ofintravitreal levofloxacin in an experimental model of endophthalmitis. BRITISH JOURNAL OF OPHTHALMOLOGY. 2008. 92(5). 678-682
[24] Spangler S. K., Lin G., Jacobs M. R., Appelbaum P. C. Postantibiotic effect of levofloxacin against pneumococci. DRUGS. 1999. 58. 378-380
[25] Pendland S. L., Diaz-Linares M., Garey K. W., Woodward J. G., Ryu S., Danziger L. H. Bactericidal activity and postantibiotic effect of levofloxacin against anaerobes. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY. 1999. 43(10). 2547-2549
[26] Bailac S., Ballesteros O., Jimenez-Lozano E., Barron D., Sanz-Nebot V., Navalon A., Vilchez J. L., Barbosa J. Determination of quinolones in chicken tissues by liquid chromatography with ultraviolet absorbance detection. JOURNAL OF CHROMATOGRAPHY A. 2004. 1029(1-2). 145-151
[27] Altiokka G., Atkosar Z., Can N. O. The determination of levofloxacin by flow injection analysis using UV detection, potentiometry, and conductometry in pharmaceutical preparations. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2002. 30(3). 881-885
[28] Agrawal O. D., Shirkhedkar A. A., Surana S. J. Two UV-spectrophotometric methods for simultaneous, determination of levofloxacin and ambroxol in tablets. ASIAN JOURNAL OF CHEMISTRY. 2008. 20(3). 1957-1960
[29] Gonzalez J.A.O., Mochon M. C., de la Rosa F. J. B. Simultaneous determination of cefepime and the quinolones garenoxacin, moxifloxacin and levofloxacin in human urine by HPLC-UV. MICROCHIMICA ACTA. 2005. 151(1-2). 39-45
[30] Hurtado F. K., Nogueira D. R., Bortolini F., da Silva L. M., Zimmermann E., Souza NJE, de Melo J., Bueno Rolim C. M. Determination of levofloxacin in a pharmaceutical injectable formulation by using HPLC and UV spectrophotometric methods. JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES. 2007. 30(13-16). 1981-1989
[31]袁耀佐,张玫.用微生物方法测定盐酸左氧氟沙星的含量.中国抗生素杂志. 1999. 24(3). 235-237
[32]齐菲.旋光法测定盐酸左氧氟沙星片的含量.药学实践杂志. 2003. 21(5). 291-293
[33] Salem A. A., Mossa H. A., Barsoum B. N. Quantitative determinations of levofloxacin and rifampicin in pharmaceutical and urine samples using nuclearmagnetic resonance spectroscopy. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. 2005. 62(1-3). 466-472
[34] Hurtado F. K., Nogueira D. R., Bortolini F., da Silva L. M., Zimmermann E., Souza N.J.E., de Melo J., Bueno Rolim C. M. Determination of levofloxacin in a pharmaceutical injectable formulation by using HPLC and UV spectrophotometric methods. JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES. 2007. 30(13-16). 1981-1989
[35] Namur S., Carino L., Gonzalez-de La Parra M. Development and validation of a densitometric HPTLC method for quantitative analysis of levofloxacin in human plasma. JPC-JOURNAL OF PLANAR CHROMATOGRAPHY-MODERN TLC. 2008. 21(3). 209-212
[36] Santoro M.I., Kassab N. M., Singh A. K., Kedor-Hackmam E.R.M. Quantitative determination of gatifloxacin, levofloxacin, lomefloxacin and pefloxacin fluoroquinolonic antibiotics in pharmaceutical preparations by high-performance liquid chromatography. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2006. 40(1). 179-184
[37]国家药典委员会.中国药典2000年版(一部)新增与修订内容汇编(初稿). 1999. 195
[38]白玲,倪永年.主成分-偏最小二乘动力学分光光度法同时测定农药两组分.分析试验室. 2002. 21(05). 39-43
[39]庞涛涛,姚建斌,杜黎明.人工神经网络分类鉴别苦丁茶红外光谱.光谱学与光谱分析. 2007. 27(07). 1336-1340
[40]飞思科技产品研发中心.小波分析理论与MATLAB 7实现.电子工业出版社. 2005. 470
[41]王国庆,邵学广.离散小波变换-遗传算法-交互检验法用于近红外光谱数据的高倍压缩与变量筛选.分析化学. 2005. 33(02). 198-200
[42]飞思科技产品研发中心.神经网络理论与MATLAB 7实现.电子工业出版社. 2005. 322
[43]张战伟,陈永华,宋传敏,李俊停.紫外分光光度法测定复方甲磺酸培氟沙星注射液含量.中国兽药杂志. 2004. 38(12). 16-20
[44]韩业超,郝庚,徐晓伟,王玉光.紫外吸收系数法测定甲磺酸培氟沙星的含量.吉林畜牧兽医. 2004. 27(01). 53-54
[45]李林秋,施爱红,邓必阳.毛细管电泳与电致化学发光结合用于人尿中甲磺酸培氟沙星的测定研究.分析测试学报. 2008. (07). 718-721
[46]李彦威,魏文珑,王志忠,王晋辉.甲磺酸培氟沙星的示波极谱滴定法研究(英文).中国抗生素杂志. 2002. 27(11). 666-669
[47]张素梅,杜素梅,李耀维.交流示波极谱滴定法测定甲磺酸培氟沙星.中国药学杂志. 1998. 33(10). 623
[48]黄淑萍,陈亮,马丽花.抗菌素甲磺酸培氟沙星荧光分析法的研究.光谱学与光谱分析. 1997. 17(02). 45-47
[49]靳建中,金文第,刘桂兰.荧光法测定甲磺酸培氟沙星片的含量.华西药学杂志. 1994. 9(04). 241-249
[50]冯娟.高效液相色谱法测定甲磺酸培氟沙星注射液的含量.医药世界. 2007. (09). 144-145
[51]孙艳云.高效液相色谱法测定甲磺酸培氟沙星注射液的含量.中国现代药物应用. 2007. 1(05). 24-25
[52]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.中国轻工业出版社. 2005. 8
[53] Owens B., McCann M. E. E., McCracken K. J., Park R. S. Prediction of wheat chemical and physical characteristics and nutritive value by near-infrared reflectance spectroscopy. BRITISH POULTRY SCIENCE. 2009. 50(1). 103-122
[54] Lee J. H., Choung M. G. Determination of Protein Content in Pea by Near Infrared Spectroscopy. FOOD SCIENCE AND BIOTECHNOLOGY. 2009. 18(1). 60-65
[55] Luo X. F., Yu X., Wu X. M., Cheng Y. Y., Qu H. B. Rapid determination of Paeoniae Radix using near infrared spectroscopy. MICROCHEMICAL JOURNAL. 2008. 90(1). 8-12
[56] Li Y. Z., Min S. G., Liu X. Applications of near-infrared spectroscopy to analysis of traditional Chinese herbal medicine. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2008. 28(7). 1549-1553
[57] Lu J., Xiang B. R., Liu H., Xiang S. Y., Xie S. F., Deng H. S. Application of two-dimensional near-infrared correlation spectroscopy to the discrimination of Chinese herbal medicine of different geographic regions. SPECTROCHIMICAACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. 2008. 69(2). 580-586
[58] Balabin R. M., Safieva R. Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data. FUEL. 2008. 87(7). 1096-1101
[59] Balabin R. M., Safieva R. Z., Lomakina E. I. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. 2007. 88(2). 183-188
[60] Christy C. D. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. COMPUTERS AND ELECTRONICS IN AGRICULTURE. 2008. 61(1). 10-19
[61]徐伟雄,谢沐风.盐酸西替利嗪含量测定方法的研讨.中国医药工业杂志. 2003. 34(07). 343-344
[62]陈继川,姬长友,蒋耀光,李雪蕾,陈亮,傅若秋.盐酸左西替利嗪对124例变应性鼻炎的前瞻性临床研究.重庆医学. 2006. 35(04). 364-366
[63]漆军,虞瑞尧,李恒进,陈雷,王荣光,汪磊,白景,张可英,邹先彪.国产盐酸西替利嗪治疗荨麻疹和过敏性鼻炎的多中心验证研究.军医进修学院学报. 2004. 25(01). 44-46
[64]舒向梅.缓释茶碱与盐酸西替利嗪治疗小儿咳嗽变异性哮喘疗效观察.儿科药学杂志. 2003. 9(03). 35-36
[65]蔡永宁,罗永艾,冯玉麟,罗炎杰.国产盐酸西替利嗪改善支气管哮喘患者肺通气功能的研究.华西医学. 1999. 14(03). 363-364
[66]江美云.盐酸西替利嗪引起不良反应.海峡药学. 2000. 12(04). 123
[67]高允生,姜隆梅,朱玉云,赵晓民,王德才,李娟.反相高效液相色谱法测定盐酸西替利嗪的血药浓度.泰山医学院学报. 2004. 25(06). 580-582
[68]王忠民,姜红,任耘,张炜旭.高效液相色谱法测定盐酸西替利嗪片的含量及溶出度.中国现代应用药学. 2006. 23(08). 791-793
[69]李菁,余俊,侯飞燕.紫外分光光度法测定盐酸左西替利嗪片的含量.怀化学院学报. 2006. 25(08) 65-66
[70]李文魁,张如意,肖培根.淫羊藿属药用植物研究进展.国外医药.植物药分册. 1993. 8(04) 147-154
[71] Colic M., Doslov-Kokorus Z., Backovic A., Stojanovic D., Antic-Stankovic J., Kovacevic N. Evaluation of the stimulatory effect of Epimedium alpinum L. methanolic extract on the immune response in vivo. PHARMAZIE. 2007. 62(9). 705-708
[72] Anon. The effect of Epimedium brevicornum Maxim extract on clitoral intracavernous pressure in the rabbit. EUROPEAN UROLOGY SUPPLEMENTS. 2006. 5(2). 27
[73] Chen K. K., Chiu J. H. Effect of Epimedium brevicornum Maxim extract on elicitation of penile erection in the rat. UROLOGY. 2006. 67(3). 631-635
[74] Zhang G., Qin L., Shi Y. Y. Epimedium-derived phytoestrogen flavonoids exert beneficial effect on preventing bone loss in late postmenopausal women: A 24-month randomized, double-blind and placebo-controlled trial. JOURNAL OF BONE AND MINERAL RESEARCH. 2007. 22(7). 1072-1079
[75] Zhang X. Y., Chen F. H., Wei P. H., Ni J. Z. Studies on the effects of epimedium extract on erythrocytes. CHINESE CHEMICAL LETTERS. 2006. 17(8). 1105-1108
[76]陈肖家,张庆文,季晖,王一涛,屠鹏飞,李绍平.紫外分光光度法和高效液相色谱法测定淫羊藿总黄酮含量的比较研究.药物分析杂志. 2007. 27(05). 625-630
[77]朱升,欧海军,赵淑儿,左华庭,祝金龙.高效液相色谱法法测定不同产地淫羊藿中淫羊藿苷的含量.时珍国医国药. 2008. 19(09). 2080-2081
[78]徐文芬,何顺志,黄敏,王悦云.高效液相色谱法测定贵州产淫羊藿药材不同药用部位中淫羊藿苷的含量.中国实验方剂学杂志. 2007. 13(05) 1-3
[79] Pozharitskaya O. N., Karlina M. V., Shikov A. N., Kosman V. M., Makarova M. N., Makarov V. G. Determination of icariin in rat plasma by reverse-phase high-performance liquid chromatography after oral administration of a lipid-based suspension of Epimedium koreanum extract. BIOMEDICAL CHROMATOGRAPHY. 2008. 22(6). 625-629
[80] Pozharitskaya O. N., Kosman V. M., Shikov A. N., Demchenko D. V., Eschenko A. Y., Makarov V. G. Comparison between HPLC and HPTLC densitometry for the determination of icariiin from Epimedium koreanum extracts. JOURNAL OF SEPARATION SCIENCE. 2007. 30(5). 708-712
[81] Williams P. C., Norris K. H., Sobering D. C. Determination of protein and moisture in wheat and barley by near-infrared transmission. Jorurnal Of AgriculturalAnd Food Chemistry. 1985. 33(2). 239-244
[82] Sinelli N., Spinardi A., Di Egidio V., Mignani I., Casiraghi E. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. POSTHARVEST BIOLOGY AND TECHNOLOGY. 2008. 50(1). 31-36
[83] Moros J., Martinez-Sanchez M. J., Perez-Sirvent C., Garrigues S., de la Guardia M. Testing of the Region of Murcia soils by near infrared diffuse reflectance spectroscopy and chemometrics. TALANTA. 2009. 78(2). 388-398
[84] Michel K., Terhoeven-Urselmans T., Nitschke R., Steffan P., Ludwig B. Use of near- and mid-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils. JOURNAL OF PLANT NUTRITION AND SOIL SCIENCE-ZEITSCHRIFT FUR PFLANZENERNAHRUNG UND BODENKUNDE. 2009. 172(1). 63-70
[85] Wu Y. W., Sun S. Q., Zhou Q., Leung H. W. Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua Oil. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2008. 46(3). 498-504
[86] Li Y. Z., Min S. G., Liu X. Applications of near-infrared spectroscopy to analysis of traditional Chinese herbal medicine. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2008. 28(7). 1549-1553
[87] Yan S. K., Luo G. A., Wang Y. M., Cheng Y. Y. Study on real-time control of extraction procedure of furitus gardenia by near infrared spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2006. 26(6). 1026-1030
[88]邵学广,许禄.化学计量学方法.科学出版社. 2004. 565
[89]罗一帆,郭振飞,朱振宇,王川丕,江和源,韩宝瑜.近红外光谱测定茶叶中茶多酚和茶多糖的人工神经网络模型研究.光谱学与光谱分析. 2005. 25(08). 1230-1233
[90]史锁芳,韩树人.肺宁颗粒治疗支气管炎咳嗽34例临床观察.国医论坛. 2005. 20(01). 23-25
[91]何华,李平,席英贤,张建林.小儿肺咳宁颗粒临床疗效初步观察.兰州医学院学报. 2002. 28(03). 73
[92]南敏伦,赫玉芳,刘静月,赵全成. HPLC法测定不同产地返魂草中绿原酸的含量.沈阳药科大学学报. 2008. 25(02). 119-121
[93]卫昌华.高效液相色谱法测定返魂草颗粒中绿原酸的含量.中国实验方剂学杂志. 2008. 14(04). 15-16
[94]王继彦,李丽静,张莲珠. HPLC法测定返魂草及其注射剂中绿原酸.中草药. 2005. 36(08). 1166-1167
[95]韩旻.在线近红外光谱法测定乙烯装置进料石脑油的芳烃含量.工业控制计算机. 2008. 21(10). 43-44
[96]杨辉华,王勇,吴云鸣,史晓浩,胡坪,梁琼麟,王义明,罗国安.丹参多酚酸盐柱层析过程的近红外光谱在线检测及质量控制.中成药. 2008. 30(03). 409-412
[97]蔡绍松,武卫红,王宁,董海平.黄芪水提液浓缩过程的AOTF-近红外光谱法在线分析.中国医药工业杂志. 2008. 37(07). 527-529
[98]吴小兵.在线近红外光谱法快速测定重整生成油中的C_5含量.现代科学仪器. 2008. (02). 59-61
[99]倪力军,史晓浩,高秀蛟,王宁. NIR在线检测、分析技术在丹参水提过程质量监控中的应用.中国药学杂志. 2004. 39(08). 628-630
[100]胡钢亮,吕秀阳,程柯,刘丹青.近红外光谱在线检测技术在中药领域中的应用展望.中国中药杂志. 2003. 28(12). 1117-1119
[2]陆婉珍.现代近红外光谱分析技术.中国石化出版社. 2000. 260
[3] Davies T. The history of near infrared spectroscopic analysis: Past, present and future–“From sleeping technique to the morning star of spectroscopy”. ANALUSIS MAGAZINE. 1998. 26(4). 17-19
[4]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.北京中国轻工业出版社. 2005. 113
[5] Lunardelli M., Marchi G., Martiis F., Application of near infrared reflectance spectroscopy (NIRS) to process control in the pharmaceutical industry. BOLLETTINO CHIMICO FARMACEUTICO. 127(1). 13-19
[6] Blanco M., Romero M. A. Near-infrared libraries in the pharmaceutical industry: a solution for identity confirmation. ANALYST. 2001. 126(12). 2212-2217
[7] Blanco M., Coello J., Eustaquio A., Iturriaga H., Maspoch S. Development and validation of a method for the analysis of a pharmaceutical preparation by near-infrared diffuse reflectance spectroscopy. JOURNAL OF PHARMACEUTICAL SCIENCES. 1999. 88(5). 551-556
[8] Blanco M., Villar A. Development and validation of a method for the polymorphic analysis of pharmaceutical preparations using near infrared spectroscopy. JOURNAL OF PHARMACEUTICAL SCIENCES. 2003. 92(4). 823-830
[9] Dreassi E., Corti G., Ceramelli P. Near-infrared reflectance spectrometry in the determination of the physical state of primary materials in pharmaceutical production. ANALYST. 1995. 120. 1005-1008
[10] Kramer K., Ebel S. Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients. ANALYTICA CHIMICA ACTA. 2000. 420(2). 155-161
[11] Dempster M. A., Jones J.A., Last I.R. Near-infrared methods for the identification of tablets in clinical trial supplies. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 1993. 11(11-12). 1087-1092
[12] Scafi S.H.F., Pasquini C. Identification of counterfeit drugs using near-infrared spectroscopy. ANALYST. 2001. 126(12). 2218-2224
[13] Corti P., Dreassi E., Murratzu C., Corbini G., Ballerini L., Gravina S. Application of NIRS to the quantity control of pharmaceuticals ketoprofen assay in different pharmaceutical formulae. PHARMACEUTICA ACTA HELVETIAe. 1989. 64(5-6). 140-145
[14] Tudor A. M., Church S. J., Hendra P. J., Davies M. C., Melia C. D. Qualitative and quantitative analysis of chlorpropamide polymorphic mixtures by near infrared fourier transform raman spectroscopy. PHARMACEUTICAL RESEARCH. 1993. 10(12). 1772-1776
[15] Woo Y. A., Kim H. J., Cho J., Chung H. Discrimination of herbal medicines according to geographical origin with near infrared reflectance spectroscopy and pattern recognition techniques. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 1999. 21(2). 407-413
[16] Woo Y. A., Cho C. H., Kim H. J., Yang J. S., Seong K. Y. Classification of cultivation area of ginseng by near infrared spectroscopy and ICP-AES. MICROCHEMICAL JOURNAL. 2002. 73(3). 299-306
[17] Laasonen M., Harmia-Pulkkinen T., Simard C. L., Michiels E., Rasanen M., Vuorela H. Fast identification of Echinacea purpurea dried roots using near-infrared spectroscopy. ANALYTICAL CHEMISTRY. 2002. 74(11). 2493-2499
[18] Chen Y., S?rensen L. K. Determination of marker constituents in radix Glycyrrhizae and radix Notoginseng by near infrared spectroscopy. FRESENIUS' JOURNAL OF ANALYTICAL CHEMISTRY. 367(5). 491-496
[19] Ikeda-Dantsuji Y., Konomi I., Nagayama A. Effects of levofloxacin and doxycycline on interleukin-6 production of Chlamydia trachomatis-infected human synovial fibroblasts. CHEMOTHERAPY. 2007. 53(5). 332-337
[20] Argyropoulos A. N., Doumas K., Farmakis A., Liakatas I., Gkialas I., Lykourinas M. Time of administration of a single dose of oral levofloxacin and its effect in infectious complications from transrectal prostate biopsy. INTERNATIONAL UROLOGY AND NEPHROLOGY. 2007. 39(3). 897-903
[21] Kelesidis T., Canseco E. Levofloxacin-induced Hypoglycemia: A Rare but Life-threatening Side Effect of a Widely Used Antibiotic. AMERICAN JOURNAL OF MEDICINE. 2009. 122(3). E3-E4
[22] Anderson V. R., Perry C. M. Levofloxacin - A review of its use as a high-dose, short-course treatment for bacterial infection. DRUGS. 2008. 68(4). 535-565
[23] Ferrer C., Rodriguez A., Abad J. L., Fernandez J., Alio J. L. Bactericidal effect ofintravitreal levofloxacin in an experimental model of endophthalmitis. BRITISH JOURNAL OF OPHTHALMOLOGY. 2008. 92(5). 678-682
[24] Spangler S. K., Lin G., Jacobs M. R., Appelbaum P. C. Postantibiotic effect of levofloxacin against pneumococci. DRUGS. 1999. 58. 378-380
[25] Pendland S. L., Diaz-Linares M., Garey K. W., Woodward J. G., Ryu S., Danziger L. H. Bactericidal activity and postantibiotic effect of levofloxacin against anaerobes. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY. 1999. 43(10). 2547-2549
[26] Bailac S., Ballesteros O., Jimenez-Lozano E., Barron D., Sanz-Nebot V., Navalon A., Vilchez J. L., Barbosa J. Determination of quinolones in chicken tissues by liquid chromatography with ultraviolet absorbance detection. JOURNAL OF CHROMATOGRAPHY A. 2004. 1029(1-2). 145-151
[27] Altiokka G., Atkosar Z., Can N. O. The determination of levofloxacin by flow injection analysis using UV detection, potentiometry, and conductometry in pharmaceutical preparations. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2002. 30(3). 881-885
[28] Agrawal O. D., Shirkhedkar A. A., Surana S. J. Two UV-spectrophotometric methods for simultaneous, determination of levofloxacin and ambroxol in tablets. ASIAN JOURNAL OF CHEMISTRY. 2008. 20(3). 1957-1960
[29] Gonzalez J.A.O., Mochon M. C., de la Rosa F. J. B. Simultaneous determination of cefepime and the quinolones garenoxacin, moxifloxacin and levofloxacin in human urine by HPLC-UV. MICROCHIMICA ACTA. 2005. 151(1-2). 39-45
[30] Hurtado F. K., Nogueira D. R., Bortolini F., da Silva L. M., Zimmermann E., Souza NJE, de Melo J., Bueno Rolim C. M. Determination of levofloxacin in a pharmaceutical injectable formulation by using HPLC and UV spectrophotometric methods. JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES. 2007. 30(13-16). 1981-1989
[31]袁耀佐,张玫.用微生物方法测定盐酸左氧氟沙星的含量.中国抗生素杂志. 1999. 24(3). 235-237
[32]齐菲.旋光法测定盐酸左氧氟沙星片的含量.药学实践杂志. 2003. 21(5). 291-293
[33] Salem A. A., Mossa H. A., Barsoum B. N. Quantitative determinations of levofloxacin and rifampicin in pharmaceutical and urine samples using nuclearmagnetic resonance spectroscopy. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. 2005. 62(1-3). 466-472
[34] Hurtado F. K., Nogueira D. R., Bortolini F., da Silva L. M., Zimmermann E., Souza N.J.E., de Melo J., Bueno Rolim C. M. Determination of levofloxacin in a pharmaceutical injectable formulation by using HPLC and UV spectrophotometric methods. JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES. 2007. 30(13-16). 1981-1989
[35] Namur S., Carino L., Gonzalez-de La Parra M. Development and validation of a densitometric HPTLC method for quantitative analysis of levofloxacin in human plasma. JPC-JOURNAL OF PLANAR CHROMATOGRAPHY-MODERN TLC. 2008. 21(3). 209-212
[36] Santoro M.I., Kassab N. M., Singh A. K., Kedor-Hackmam E.R.M. Quantitative determination of gatifloxacin, levofloxacin, lomefloxacin and pefloxacin fluoroquinolonic antibiotics in pharmaceutical preparations by high-performance liquid chromatography. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2006. 40(1). 179-184
[37]国家药典委员会.中国药典2000年版(一部)新增与修订内容汇编(初稿). 1999. 195
[38]白玲,倪永年.主成分-偏最小二乘动力学分光光度法同时测定农药两组分.分析试验室. 2002. 21(05). 39-43
[39]庞涛涛,姚建斌,杜黎明.人工神经网络分类鉴别苦丁茶红外光谱.光谱学与光谱分析. 2007. 27(07). 1336-1340
[40]飞思科技产品研发中心.小波分析理论与MATLAB 7实现.电子工业出版社. 2005. 470
[41]王国庆,邵学广.离散小波变换-遗传算法-交互检验法用于近红外光谱数据的高倍压缩与变量筛选.分析化学. 2005. 33(02). 198-200
[42]飞思科技产品研发中心.神经网络理论与MATLAB 7实现.电子工业出版社. 2005. 322
[43]张战伟,陈永华,宋传敏,李俊停.紫外分光光度法测定复方甲磺酸培氟沙星注射液含量.中国兽药杂志. 2004. 38(12). 16-20
[44]韩业超,郝庚,徐晓伟,王玉光.紫外吸收系数法测定甲磺酸培氟沙星的含量.吉林畜牧兽医. 2004. 27(01). 53-54
[45]李林秋,施爱红,邓必阳.毛细管电泳与电致化学发光结合用于人尿中甲磺酸培氟沙星的测定研究.分析测试学报. 2008. (07). 718-721
[46]李彦威,魏文珑,王志忠,王晋辉.甲磺酸培氟沙星的示波极谱滴定法研究(英文).中国抗生素杂志. 2002. 27(11). 666-669
[47]张素梅,杜素梅,李耀维.交流示波极谱滴定法测定甲磺酸培氟沙星.中国药学杂志. 1998. 33(10). 623
[48]黄淑萍,陈亮,马丽花.抗菌素甲磺酸培氟沙星荧光分析法的研究.光谱学与光谱分析. 1997. 17(02). 45-47
[49]靳建中,金文第,刘桂兰.荧光法测定甲磺酸培氟沙星片的含量.华西药学杂志. 1994. 9(04). 241-249
[50]冯娟.高效液相色谱法测定甲磺酸培氟沙星注射液的含量.医药世界. 2007. (09). 144-145
[51]孙艳云.高效液相色谱法测定甲磺酸培氟沙星注射液的含量.中国现代药物应用. 2007. 1(05). 24-25
[52]严衍禄,赵龙莲,韩东海,杨曙明.近红外光谱分析基础与应用.中国轻工业出版社. 2005. 8
[53] Owens B., McCann M. E. E., McCracken K. J., Park R. S. Prediction of wheat chemical and physical characteristics and nutritive value by near-infrared reflectance spectroscopy. BRITISH POULTRY SCIENCE. 2009. 50(1). 103-122
[54] Lee J. H., Choung M. G. Determination of Protein Content in Pea by Near Infrared Spectroscopy. FOOD SCIENCE AND BIOTECHNOLOGY. 2009. 18(1). 60-65
[55] Luo X. F., Yu X., Wu X. M., Cheng Y. Y., Qu H. B. Rapid determination of Paeoniae Radix using near infrared spectroscopy. MICROCHEMICAL JOURNAL. 2008. 90(1). 8-12
[56] Li Y. Z., Min S. G., Liu X. Applications of near-infrared spectroscopy to analysis of traditional Chinese herbal medicine. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2008. 28(7). 1549-1553
[57] Lu J., Xiang B. R., Liu H., Xiang S. Y., Xie S. F., Deng H. S. Application of two-dimensional near-infrared correlation spectroscopy to the discrimination of Chinese herbal medicine of different geographic regions. SPECTROCHIMICAACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY. 2008. 69(2). 580-586
[58] Balabin R. M., Safieva R. Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data. FUEL. 2008. 87(7). 1096-1101
[59] Balabin R. M., Safieva R. Z., Lomakina E. I. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. 2007. 88(2). 183-188
[60] Christy C. D. Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. COMPUTERS AND ELECTRONICS IN AGRICULTURE. 2008. 61(1). 10-19
[61]徐伟雄,谢沐风.盐酸西替利嗪含量测定方法的研讨.中国医药工业杂志. 2003. 34(07). 343-344
[62]陈继川,姬长友,蒋耀光,李雪蕾,陈亮,傅若秋.盐酸左西替利嗪对124例变应性鼻炎的前瞻性临床研究.重庆医学. 2006. 35(04). 364-366
[63]漆军,虞瑞尧,李恒进,陈雷,王荣光,汪磊,白景,张可英,邹先彪.国产盐酸西替利嗪治疗荨麻疹和过敏性鼻炎的多中心验证研究.军医进修学院学报. 2004. 25(01). 44-46
[64]舒向梅.缓释茶碱与盐酸西替利嗪治疗小儿咳嗽变异性哮喘疗效观察.儿科药学杂志. 2003. 9(03). 35-36
[65]蔡永宁,罗永艾,冯玉麟,罗炎杰.国产盐酸西替利嗪改善支气管哮喘患者肺通气功能的研究.华西医学. 1999. 14(03). 363-364
[66]江美云.盐酸西替利嗪引起不良反应.海峡药学. 2000. 12(04). 123
[67]高允生,姜隆梅,朱玉云,赵晓民,王德才,李娟.反相高效液相色谱法测定盐酸西替利嗪的血药浓度.泰山医学院学报. 2004. 25(06). 580-582
[68]王忠民,姜红,任耘,张炜旭.高效液相色谱法测定盐酸西替利嗪片的含量及溶出度.中国现代应用药学. 2006. 23(08). 791-793
[69]李菁,余俊,侯飞燕.紫外分光光度法测定盐酸左西替利嗪片的含量.怀化学院学报. 2006. 25(08) 65-66
[70]李文魁,张如意,肖培根.淫羊藿属药用植物研究进展.国外医药.植物药分册. 1993. 8(04) 147-154
[71] Colic M., Doslov-Kokorus Z., Backovic A., Stojanovic D., Antic-Stankovic J., Kovacevic N. Evaluation of the stimulatory effect of Epimedium alpinum L. methanolic extract on the immune response in vivo. PHARMAZIE. 2007. 62(9). 705-708
[72] Anon. The effect of Epimedium brevicornum Maxim extract on clitoral intracavernous pressure in the rabbit. EUROPEAN UROLOGY SUPPLEMENTS. 2006. 5(2). 27
[73] Chen K. K., Chiu J. H. Effect of Epimedium brevicornum Maxim extract on elicitation of penile erection in the rat. UROLOGY. 2006. 67(3). 631-635
[74] Zhang G., Qin L., Shi Y. Y. Epimedium-derived phytoestrogen flavonoids exert beneficial effect on preventing bone loss in late postmenopausal women: A 24-month randomized, double-blind and placebo-controlled trial. JOURNAL OF BONE AND MINERAL RESEARCH. 2007. 22(7). 1072-1079
[75] Zhang X. Y., Chen F. H., Wei P. H., Ni J. Z. Studies on the effects of epimedium extract on erythrocytes. CHINESE CHEMICAL LETTERS. 2006. 17(8). 1105-1108
[76]陈肖家,张庆文,季晖,王一涛,屠鹏飞,李绍平.紫外分光光度法和高效液相色谱法测定淫羊藿总黄酮含量的比较研究.药物分析杂志. 2007. 27(05). 625-630
[77]朱升,欧海军,赵淑儿,左华庭,祝金龙.高效液相色谱法法测定不同产地淫羊藿中淫羊藿苷的含量.时珍国医国药. 2008. 19(09). 2080-2081
[78]徐文芬,何顺志,黄敏,王悦云.高效液相色谱法测定贵州产淫羊藿药材不同药用部位中淫羊藿苷的含量.中国实验方剂学杂志. 2007. 13(05) 1-3
[79] Pozharitskaya O. N., Karlina M. V., Shikov A. N., Kosman V. M., Makarova M. N., Makarov V. G. Determination of icariin in rat plasma by reverse-phase high-performance liquid chromatography after oral administration of a lipid-based suspension of Epimedium koreanum extract. BIOMEDICAL CHROMATOGRAPHY. 2008. 22(6). 625-629
[80] Pozharitskaya O. N., Kosman V. M., Shikov A. N., Demchenko D. V., Eschenko A. Y., Makarov V. G. Comparison between HPLC and HPTLC densitometry for the determination of icariiin from Epimedium koreanum extracts. JOURNAL OF SEPARATION SCIENCE. 2007. 30(5). 708-712
[81] Williams P. C., Norris K. H., Sobering D. C. Determination of protein and moisture in wheat and barley by near-infrared transmission. Jorurnal Of AgriculturalAnd Food Chemistry. 1985. 33(2). 239-244
[82] Sinelli N., Spinardi A., Di Egidio V., Mignani I., Casiraghi E. Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy. POSTHARVEST BIOLOGY AND TECHNOLOGY. 2008. 50(1). 31-36
[83] Moros J., Martinez-Sanchez M. J., Perez-Sirvent C., Garrigues S., de la Guardia M. Testing of the Region of Murcia soils by near infrared diffuse reflectance spectroscopy and chemometrics. TALANTA. 2009. 78(2). 388-398
[84] Michel K., Terhoeven-Urselmans T., Nitschke R., Steffan P., Ludwig B. Use of near- and mid-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils. JOURNAL OF PLANT NUTRITION AND SOIL SCIENCE-ZEITSCHRIFT FUR PFLANZENERNAHRUNG UND BODENKUNDE. 2009. 172(1). 63-70
[85] Wu Y. W., Sun S. Q., Zhou Q., Leung H. W. Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua Oil. JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS. 2008. 46(3). 498-504
[86] Li Y. Z., Min S. G., Liu X. Applications of near-infrared spectroscopy to analysis of traditional Chinese herbal medicine. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2008. 28(7). 1549-1553
[87] Yan S. K., Luo G. A., Wang Y. M., Cheng Y. Y. Study on real-time control of extraction procedure of furitus gardenia by near infrared spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS. 2006. 26(6). 1026-1030
[88]邵学广,许禄.化学计量学方法.科学出版社. 2004. 565
[89]罗一帆,郭振飞,朱振宇,王川丕,江和源,韩宝瑜.近红外光谱测定茶叶中茶多酚和茶多糖的人工神经网络模型研究.光谱学与光谱分析. 2005. 25(08). 1230-1233
[90]史锁芳,韩树人.肺宁颗粒治疗支气管炎咳嗽34例临床观察.国医论坛. 2005. 20(01). 23-25
[91]何华,李平,席英贤,张建林.小儿肺咳宁颗粒临床疗效初步观察.兰州医学院学报. 2002. 28(03). 73
[92]南敏伦,赫玉芳,刘静月,赵全成. HPLC法测定不同产地返魂草中绿原酸的含量.沈阳药科大学学报. 2008. 25(02). 119-121
[93]卫昌华.高效液相色谱法测定返魂草颗粒中绿原酸的含量.中国实验方剂学杂志. 2008. 14(04). 15-16
[94]王继彦,李丽静,张莲珠. HPLC法测定返魂草及其注射剂中绿原酸.中草药. 2005. 36(08). 1166-1167
[95]韩旻.在线近红外光谱法测定乙烯装置进料石脑油的芳烃含量.工业控制计算机. 2008. 21(10). 43-44
[96]杨辉华,王勇,吴云鸣,史晓浩,胡坪,梁琼麟,王义明,罗国安.丹参多酚酸盐柱层析过程的近红外光谱在线检测及质量控制.中成药. 2008. 30(03). 409-412
[97]蔡绍松,武卫红,王宁,董海平.黄芪水提液浓缩过程的AOTF-近红外光谱法在线分析.中国医药工业杂志. 2008. 37(07). 527-529
[98]吴小兵.在线近红外光谱法快速测定重整生成油中的C_5含量.现代科学仪器. 2008. (02). 59-61
[99]倪力军,史晓浩,高秀蛟,王宁. NIR在线检测、分析技术在丹参水提过程质量监控中的应用.中国药学杂志. 2004. 39(08). 628-630
[100]胡钢亮,吕秀阳,程柯,刘丹青.近红外光谱在线检测技术在中药领域中的应用展望.中国中药杂志. 2003. 28(12). 1117-1119