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Powder diffractometric assay of two polymorphic forms of ranitidine hydrochloride
S Agatonovic-Kustrin1, V Wu, T Rades
1School of Pharmacy, University of Otago, P.O. Box 913, Dunedin, New Zealand. nena.kustrin@stonebow.otago.ac.nz
International Journal of Pharmaceutics
|July 30, 1999
Summary
Artificial neural networks (ANNs) offer a precise method for quantitative X-ray diffraction analysis of ranitidine-HCl crystalline forms. This approach surpasses conventional methods, especially at lower concentrations, improving accuracy and reducing errors in pharmaceutical analysis.
Area of Science:
- Analytical Chemistry
- Materials Science
- Computational Chemistry
Background:
- X-ray powder diffractometry (XRPD) is crucial for analyzing crystalline materials.
- Ranitidine hydrochloride (ranitidine-HCl) exists in multiple crystalline forms requiring precise quantification.
- Conventional methods for quantitative XRPD analysis can be complex and less precise at low concentrations.
Purpose of the Study:
- To develop a simple X-ray powder diffractometric method for ranitidine-HCl.
- To evaluate the application of artificial neural networks (ANNs) for quantitative XRPD analysis.
- To compare ANN performance against conventional mixture design methods.
Main Methods:
- Development of a straightforward X-ray powder diffractometry protocol.
- Implementation and training of artificial neural networks (ANNs).
- Comparative analysis using a conventional mixture design approach.
Main Results:
- ANNs demonstrated superior precision and accuracy compared to conventional methods.
- The ANN approach yielded a smaller standard deviation and relative error.
- Enhanced performance was particularly noted at lower concentrations of ranitidine-HCl.
Conclusions:
- Artificial neural networks provide a powerful and simpler alternative for quantitative XRPD analysis.
- ANNs effectively model the non-linear relationships inherent in diffractometric data.
- This method offers improved accuracy and precision for crystalline form analysis in pharmaceutical quality control.