Quantitative analysis of polymorphic mixtures of carbamazepine by Raman spectroscopy and principal components analysis

被引:63
作者
Strachan, CJ
Pratiwi, D
Gordon, KC
Rades, T [1 ]
机构
[1] Univ Otago, Sch Pharm, Solid State Res Grp, Dunedin, New Zealand
[2] Univ Otago, Dept Chem, Dunedin, New Zealand
关键词
carbamazepine; polymorphism; principal components analysis; quantitative analysis;
D O I
10.1002/jrs.1140
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
The polymorphic behaviour of drugs is a major concern of the pharmaceutical industry as it may have considerable formulation, therapeutic, legal and commercial implications. It is therefore crucial to be able adequately to identify and quantify different polymorphic forms of drugs as early as possible in the drug discovery and development process. The aim of the present study was to investigate the application of Raman spectroscopy and principal components analysis (PCA) to the quantitative analysis of polymorphic mixtures of carbamazepine forms III and I, based on both individual peak areas and heights in the Raman spectra and whole wavenumber ranges. Various linear regressions indicated that in general using every fourth wavenumber value of a larger region of the Raman spectrum gives better quantitative results than choosing defined peak areas and peak heights, as the spectral differences between the polymorphs were only subtle. Baseline correction of the spectral data did not appear to improve the quantitative analysis, with detection and quantification limits being the lowest (0.33 and 1.09%, respectively) and R-2 being the highest (0.96) when using every fourth wavenumber value from 2950 to 3100 cm(-1) as input data for the PCA analysis without baseline correction. This study demonstrated that PCA of Raman spectroscopic data provides a sensitive method for the quantitative analysis of polymorphic forms, with a quantitation limit of <2%, if the spectral input data are selected carefully. In the absence of clear and distinct spectral differences at certain wavenumbers between the polymorphic forms, selection of larger spectral regions that contain most of the detectable differences between the two forms appears to be the best data selection technique. Copyright (C) 2004 John Wiley Sons, Ltd.
引用
收藏
页码:347 / 352
页数:6
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