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。     

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 。     

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。     

人工神经网络方法用于多组分分光光度分析

本文介绍了人工神经网络（ANN）原理,详细讨论了网络参数的选择及其对网络预报结果的影响。并将人工神经网络应用于干扰严重的五组分体系的分光光度同时分析,其预测结果优于正交分解计算方法。     

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…     

近红外漫反射一阶导数光谱法作安体舒通质量控制研究

研究了近红外漫反射一阶导数光谱法作安休舒通粉末药品质量控制的可能性，用多变量统计分类技术，从安体舒通粉末药品的近红外漫反射一阶导数光谱，成功地鉴别了真，劣和假药，结果令人满意。     

甲氧苄胺嘧啶药物的非破坏分析

将偏最小二乘（ＰＬＳ）法同近红外漫反射光谱法结合，非破坏分析了粉末药品甲氧苄胺嘧啶。讨论了波长对ＰＬＳ定量预报能力的影响。校正样品和预测样品的预测结果相对标准误差分别为０．３３％和１．３９％。     

人工神经网络在气相色谱保留值预测上的应用

应用人工神经网络对气相色谱Ｋｏｖａｔｓ保留指数进行多点预测研究。基于人工神经网络的典型模型（ＢＰ网络）,采用文献中的数据,建立了反映溶质的Ｋｏｖａｔｓ指数与溶质的溶剂化参数以及其与固定相的相参数之间关系的数学模型,并采用多元性回归方法和人工神经网络对溶质的Ｋｏｖａｔｓ指数进行了预测研究。     

人工神经网络用于化学数据解析的研究（I）：逼近规律与过拟合

对多层前传网络的过拟合问题进行了探讨，定义了逼近误差和逼近度作为人工神经网络（ＡＮＮ）的建模评价指标，通过应用于多元非线性校正的数值模拟和实际药物光度分析数据解析，表明该指示明确，便于掌握，且能较好的地定量表述ＡＮＮ逼近规律的程度。     

神经网络—紫外光度法同时测定水中硝酸盐氮和亚硝酸盐氮

研究了硝酸盐和亚硝酸盐的紫外吸收光谱。利用基于ＢＰ算法的人工神经网络对光谱数据处理,提出同时测定水样中硝酸盐氮和亚硝酸盐氮的新方法。对人工合成试样和实际水样进行了测定。结果满意。采用均匀设计法构建试验系统及网络运行参数的选择。效果良好。     

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,…     

Nondestructive Quantitative Analysis of Analgini Powder Pharmaceutical by Near-Infrared Spectroscopy and Artifical Neural Network Technique

ABSTRACT

This paper demonstrates the usefulness of near-infrared (NIR) spectra and artificial neural network (ANN) in nondestructive quantitative analysis of pharmaceuticals. Real data sets from near-infrared reflectance spectra of analgini powder pharmaceutical were used to build up an artificial neural network to predict unknown samples. The parameters affecting the network were discussed. A new network evaluation criterion, the degree of approximation, was employed. The overfitting was discussed. Owing to the good nonlinear multivariate calibration nature of ANN, the predicted result was reliable and precise. The relative error of unknown samples was less than 2.5%     

Simultaneous determination of rifampicin and isoniazid by continuous-flow chemiluminescence with artificial neural network calibration

In this paper a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of rifampicin and isoniazid. This method is based on the different kinetic spectra of the analytes in their CL reaction with alkaline N-bromosuccinimide as oxidant. The CL intensity was measured and recorded every second from 1 to 300 s. The data obtained were processed chemometrically by use of an artificial neural network. The experimental calibration set was 20 sample solutions. The relative standard errors of prediction for both analytes were approximately 5%. The proposed method was successfully applied to the simultaneous determination of rifampicin and isoniazid in a combined pharmaceutical formulation.     

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.     

ICA方法与NIR技术用于药片中活性成分含量的测定

用独立分量分析(ICA)方法提取药片近红外光谱数据矩阵的独立成分和相应的混合矩阵, 再用BP神经网络对混合矩阵和药片中活性成分的浓度矩阵进行建模, 提出了新的药片活性成分含量测定的基于独立分量分析-神经网络回归(ICA-NNR)的近红外光谱分析方法. 通过分析独立分量数和网络中间隐层的神经元数对模型性能的影响, 分别建立三类药片定量分析的最优模型. 该方法用于实测的三类药片中活性成分含量的测定, 测试样品集的化学检测值与近红外预测值的相关系数分别达到0.962, 0.980及0.979. 结果表明, 基于ICA-NNR的近红外光谱分析方法对制药业的药片进行定量分析是可行的.     

Genetic Algorithm Based Potential Selection in Simultaneous Voltammetric Determination of Isoniazid and Hydrazine by Using Partial Least Squares (PLS) and Artificial Neural Networks (ANNs)

The voltammetric behavior of isoniazid and hydrazine at an overoxidized polypyrrole modified glassy carbon electrode has been investigated. The obtained cyclic voltammograms showed that their oxidation peaks were overlapped and it is difficult to determine them individually from a mixture without separation. To overcome this limitation, a procedure was proposed for resolution of overlapped voltammetric signals from mixtures of isoniazid and hydrazine. In this procedure, genetic algorithm was used for the selection of potentials for partial least squares. A feed forward artificial neural network with back propagation error algorithm was used to process the nonlinear relationship between currents and concentrations of hydrazine and isoniazid. The proposed method was suitable for determination of isoniazid in pharmaceutical tablets and detection of hydrazine impurities in the same samples.