扑热息痛片剂药品的近红外光谱法非破坏定量分析

现代近红外光谱分析技术将近红外光谱 (NIR)法同计算机科学和化学计量学结合 ,实现了对样品进行无损非破坏性定量分析 .该法具有速度快、操作简单及所需样品少等特点 ,能够实现样品分析的时间同步、地点同步及无损非破坏分析 .为实现生产过程中即时、在线的质量控制提供了新的手段[1 ] .本文应用人工神经网络 [2 ]与近红外漫反射光谱相结合对扑热息痛片剂药品进行了非破坏快速定量分析 .用扑热息痛片剂药品的近红外漫反射光谱数据、一阶导数光谱数据及二阶导数光谱数据分别建立了 ANN模型 ,预测未知样品 ,讨论了影响网络的因素 ,使用了新…     

近红外光谱法对头孢氨苄粉末药品的非破坏定量分析

采用近红外漫反射光谱法对头孢氨苄粉末药品中主要成分头孢氨苄进行快速、无损定量分析.采用偏最小二乘法建立近红外光谱信息与待测组分含量间的最佳数学校正模型.对3种光谱(SNV光谱、一阶导数、二阶导光谱)的预测结果进行了比较,讨论了光谱的预处理方法和主成分数对偏最小二乘法定量预测能力的影响,并对预测集样品进行预测.     

人工神经网络-近红外光谱法非破坏监测芦丁药品的质量

用近红外漫反射光谱法非破坏监测芦丁药品的质量。利用人工神经网络化学计量学分类技术，建立三层神经元的神经网络，对网络参数进行优化选择以建立最佳网络模型。根据芦丁药品的近红外漫反射光谱，成功地分辨出合格药品和不合格药品。     

近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量

采用近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量。用近红外光谱法对163个具有代表性卷烟纸样品进行测定,利用偏最小二乘法建立了卷烟纸中钠、钾、镁、钙和柠檬酸根的数学定量模型。结果表明:当卷烟纸重叠张数为15张及以上时,近红外漫反射扫描光谱无明显差异;各模型相关系数分别为0.949 6,0.982 5,0.958 1,0.930 0,0.987 9;模型交互验证均方根误差分别为0.245,0.415,0.050 5,3.08,0.533;模型外部验证平均相对偏差分别为6.63%,4.87%,6.03%,2.31%,4.58%。t-检验结果表明:5种组分显著性水平均大于0.05,预测值与测定值不存在显著性差异。     

人工神经网络-近红外光谱法用于粉末药品美的康的非破坏定量分析

1　引　　言 　　近红外光谱分析技术在药品分析中得到重视,这是因为该方法的非破坏性,样品不需预处理 ,不需分离,即可直接测定。但其各组分光谱重叠需采用一定的数据处理技术方能获得准确 分析结果。本文应用人工神经网络 进行粉末药品美的康的非破坏性快速定量分析。使用一组美的康粉末药品的近红外漫反射光 谱数据建立人工神经网络模型,预测未知样品。讨论了影响网络的各参数。采用了新的网络 评价标准——逼近度。 　　2　实验部分 　　2.1　仪器和试剂　日本岛津UV-3100型紫外可见近红外分光光度计,附件积分球。测量条件,狭缝为12 nm,扫描范围1100～2500 nm。所用试剂均符合药典标准 。     

比导数荧光光谱法测定混合芳烃

比值导数荧光光谱法是将混合物的光谱除以其中一组分的光谱得到比值光谱，利用比值光谱对波长求导所得到的比值导数光谱来达到消除干扰组分影响、分辨重叠光谱的目的。作者研究用比值导数荧光光谱法对２组分和３组分荧光光谱严重重叠的菲、、蒽混合芳烃溶液进行分析，测试效果良好，回收率为９４％～１０５％。     

近红外光谱法同时测定多种雌、孕激素

研究并建立了用近红外吸收光谱同时测定多种雌、孕激素含量的快速方法,以四氢呋喃为溶剂,测定了雌二醇、雌三醇、雌酚酮,安宫黄体酮混合溶液的近红外光谱,利用雌,孕激素类物质在6000－11000cm^-1之间的近红外吸收,用偏最小二乘法解析重叠光谱,求得各组分的含曦,结果表明近外光谱法可以有效地同时检测雌二醇,雌三醇、雌酚酮和安宫黄体酮;并讨论了多种因素对测量精度的影响。     

近红外光谱在无机微量成分分析中的应用

由于近红外光谱的独特优势, 在实际复杂样品分析中发挥了重要作用. 但由于近红外光谱的信号相对较弱, 无机离子在近红外光谱中一般没有响应, 因此难以用于微量成分特别是无机微量组分的测定. 总结了近红外光谱技术在环境、土壤、植物及生物样品分析中的应用, 说明了近红外光谱用于无机微量成分分析的原理. 由于近红外光谱技术一般通过多元校正方法进行定性定量分析, 利用组分间的相互作用或组分含量之间的相关性可以实现微量组分或无光谱响应组分的定量分析. 还总结了富集技术在近红外光谱分析中的应用, 利用富集技术可实现稀溶液中金属离子含量的快速测定, 并可以改善分析的灵敏度和检测限.     

近红外漫反射光谱聚类分析用于血竭的鉴别

建立用近红外漫反射光谱法鉴别血竭的方法，采用聚类分析方法进行分类鉴别，快速、准确地鉴别了不同产地的血竭。近红外漫反射光谱法快速、简便、无损，可用于血竭等中药的分类鉴别。     

人工神经网络—近红外光谱法用于甲氧苄胺嘧啶粉末药品的非？…

近红外(NIR)光谱分析技术已应用于制药、化妆品、烟草、食品、化学药品、聚合物、纺织品、油漆涂料、煤炭和石油工业等各个领域的质量监控.近年来,NIR光谱分析技术也应用于药品分析中,因该方法具有非破坏性,样品不需要复杂的预处理和分离即可直接测定.它可对药物进行定性和定量测定以及多晶、光学异构体和湿度的测定.近红外光谱法用于无损非破坏测定胶囊类以及片剂的研究已有报道[1,2].NIR光谱在使用中也有一定的局限性,主要是结构复杂,谱图重叠多,在进行定性和定量分析中需采用一定的数据处理才能获得准确可靠的分析结果.在定量分析中,…     

Quality control of the powder pharmaceutical samples of sulfaguanidine by using NIR reflectance spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were applied to the identification of the powder pharmaceutical samples of sulfaguanidine based on near infrared (NIR) diffuse reflectance spectra and their first derivative spectra. This work focused on the comparison of performances of the uni-output TCCCN (Uni-TCCCN) and multi-output (Multi-TCCCN) by near infrared diffuse reflectance spectra and their first derivative spectra of sulfaguanidine. The TCCCN models were verified with independent prediction samples by using the “cross-validation” method. The networks were used to discriminate qualified, un-qualified and counterfeit sulfaguanidines pharmaceutical powders. The results showed that single outputs network generally worked better than the multiple outputs networks, and the first derivative spectra were more suitable for the identification comparing with original diffuse reflectance spectra. With proper network parameters the pharmaceutical powders can be classified at rate of 100% in this work. Also, the effects of parameters and related problems were discussed.     

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.     

粉末药品安体舒通的无损定量分析——人工神经网络－近红外光谱法的应用

研究了应用人工神经网络进行粉末药品的无损定量分析,使用安体舒通粉末药品的近红外漫反射光谱数据建立人工神经网络模型,预测未知样品,讨论了影响网络的各参数,使用了逼近度作为网络新的评价标准。     

近红外光谱-人工神经网络法用于粉末药品阿斯匹林的非破坏定量分析

研究了应用人工神经网络进行粉末药品的非破坏定量分析。使用阿斯匹林粉末药品的近红外漫反射一阶导数光谱数据建立人工神经网络模型,预测未知样品。讨论了影响网络的各参数,使用了新的网络评价标准－逼近度。     

中药口服固体制剂原辅料近红外光谱数据库的构建及应用

建立了中药口服固体制剂原辅料近红外(NIR)光谱数据库,采用模式识别方法研究了NIR光谱数据在物料分类和物性预测中的应用。使用便携式近红外光谱仪快速测量149批原辅料粉末的NIR漫反射光谱数据,并录入iTCM数据库。利用主成分分析(PCA)法探究NIR光谱数据对已知结构物料的分类能力,采用偏最小二乘(PLS)法研究了NIR光谱对原辅料物性参数和直接压片片剂性能的预测能力。经标准正态变量变换(SNV)+Savitzky-Golay(SG)平滑+一阶导数处理后的NIR光谱数据对微晶纤维素、乳糖、乙基纤维素、交联聚维酮和羟丙基甲基纤维素这5类辅料的区分能力较好。NIR光谱数据与原辅料粉末粒径、密度和吸湿性的相关性较强。NIR光谱信息作为物料物理性质的补充,可提高粉末直接压片片剂性能预测模型的性能。NIR光谱数据是iTCM数据库物性参数数据的补充,物性参数与NIR光谱数据的结合能更全面地表征原辅料的性质。     

Influence of the procedure used to prepare the calibration sample set on the performance of near infrared spectroscopy in quantitative pharmaceutical analyses

Calibrating near infrared diffuse reflectance spectroscopy (NIRS) methods usually involves preparing a set of samples with a view to expanding the analyte concentration range spanned by production samples. In this work, the performances of the two procedures most frequently used for this purpose in near infrared pharmaceutical analysis, viz., synthetic samples obtained by weighing of the pure constituents of the pharmaceutical and doped samples made by under- or overdosing previously powdered production samples, were compared. Both procedures were found to provide similar results in the quantification of the active compound in the pharmaceutical, which was determined with a relative standard error of prediction (RSEP) of < 1.6%. However, the two types of sample preparation provide different spectra, which precludes the accurate quantification of synthetic samples from calibrations obtained with doped samples and vice versa. None of the mathematical pre-treatments tested with a view to reducing this different scattering (viz., second derivative, standard normal variate and orthogonal signal correction) could effectively solve this problem. This hinders accurate validation of the linearity of the procedure and makes it advisable to use doped samples which are markedly less different to production samples.     

Quality control of pharmaceuticals with NIR: From lab to process line

This work describes a general framework for assessing the active pharmaceutical ingredient (API) and excipient concentrations simultaneously in pharmaceutical dosage forms based on laboratory-scale measurements. The work explores the comprehensive development of a near infrared (NIR) analytical protocol for the quantification of the API and excipients of a pharmaceutical formulation. The samples were based on a paracetamol (API) formulation with three excipients: microcrystalline cellulose, talc, and magnesium stearate. The developed method was based on laboratory-scale samples as calibration samples and pilot-scale samples (powders and tablets) as model test samples. Both types of samples were produced according to an experimental design. The samples were measured in reflectance mode in a Fourier-transform NIR spectrometer. Additionally, a new method for determining the minimum number of calibration samples was proposed. It was concluded that the use of laboratory-scale samples to construct the calibration set is an effective way to ensure the concentration variability in the development of calibration models for industrial applications. With this method, both API and excipients can be determined in high-throughput applications in the pharmaceutical industry.     

Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis

The secondary metabolites of different Ephedra plants are various. Therefore, the discrimination of different Ephedra plants is significant. An objective, easy-to-use, rapid and pollution-free approach is proposed for discriminating Ephedra plants of different species, habitats and picking times on the basis of diffuse reflectance Fourier transform near infrared spectroscopy (FT-NIRS) measurements and multivariate analysis. The Fourier transform near infrared diffuse reflectance spectra (NIRDRS) were acquired from 37 pulverized samples of Ephedra plants put in glass vials in the near infrared (NIR) region between 10 000 and 4000 cm⁻¹, averaging 64 scans per spectrum at a resolution of 4 cm⁻¹. After spectra processing and data pre-processing, spectral data were analyzed respectively with three multivariate analysis techniques: discriminant analysis (DA), self-organizing map (SOM) and back-propagation artificial neural network (BP-ANN). The proposed method could distinguish not only the Ephedra plants of three species and two habitats but also the plants picked at different times of day without special sample treatment and the use of chemical reagents. The performance indexes of the DA model were 84.2-91.9% and the prediction accuracies of both the SOM and the BP-ANN models reached 93.3-100.0%.     

Determination of ascorbic acid in pharmaceutical preparations by near infrared reflectance spectroscopy

We developed a method for determination of ascorbic acid in pharmaceutical preparations containing various excipients by using near infrared diffuse reflectance spectroscopy and two different calibration methods, viz. stepwise multiple linear regression (SMLR) and partial least-squares (PLS) regression, which provided comparable results and resulted in prediction errors of 1-2%. However, the PLS method provided somewhat better results with the more complex samples.