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Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

作     者:Manfei Xu Luwei Zhou Qiao Zhang Zhisheng Wu Xinyuan Shi Yanjiang Qiao 

作者机构:Beijing University of Chinese MedicineP.R.China 100102 Pharmaceutical Engineering and New Drug Development of Traditional Chinese Medicine(TCM)of Ministry of EducationP.R.China 100102 Key Laboratory of TCM-information Engineering of State Administration of TCM BeijingP.R.China 100102 Beijing Key Laboratory for Basic and Development Research on Chinese Medicine BeijingP.R.China 100102 

出 版 物:《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志(英文))

年 卷 期:2016年第9卷第6期

页      面:12-20页

核心收录:

学科分类:0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基  金:supported from Beijing Municipal Government for the university a±liated with the Party Central Committee(Prof.Shi) National Natural Science Foundation of China(81303218) Doctoral Fund of Ministry of Education of China(20130013120006) Special Fund of Beijing University of Chinese Medicine(Manfei Xu) 

主  题:Near infrared chemical imaging partial least squares regression assessment of distributional homogeneity chlorpheniramine maleate 

摘      要:Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult *** could be utilized to visualize the spatial distribution of the ingredients in a *** data acquired using NIR CI instrument are hyperspectral data cube(hypercube)containing thousands of *** methodologies are necessary to transform spectral information into chemical *** least squares(PLS)method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations.A series of samples which consisted of different CPM concentrations(w/w)were compressed and hypercube data were *** spectra extracted from the hypercube were used to establish the PLS model of *** results of the model were R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.

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