Ranitidine hydrochloride X-ray assay using a neural network

A simple X-ray powder diffractometric (XRD) method with artificial neural n etworks (ANNs) for data modelling was developed to recognize and quantify t wo crystal modifications of ranitidine-HCl in mixtures and thus, provide in formation about the solid state of the bulk drug. The method was also used to quantify ranitidine-HCl from tablets in the presence of other components . An ANN consisting of three layers of neurons was trained by using a back- propagation learning rule. A sigmoid output function was used in the hidden layer to facilitate non-linear fitting. Unlike other techniques the ANN me thod described here employed pattern recognition on the entire XRD pattern. Correct classification was mainly influenced by the XRD pattern resolution . It was shown that data transformations improved the quantitative performa nce when the XRD patterns were not contaminated by other components. Only s moothed X-ray diffractograms were required to distinguish between the two c rystalline forms in a mixture. In the case of ranitidine-HCl quantification from tablets, where significant interference with tablet excipients was pr esent; better results were obtained without data transformations. The train ed ANN perfectly quantified ranitidine-HCl polymorphic forms from mixtures (mean sum of squared error was less than 0.02%) and ranitidine-HCl form 1 f rom tablets (recovery = 98.65). Excellent quantification performance of the ANN analysis, demonstrated in this study, serves as an indication of the b road potential of neural networks in pattern analysis. While the system des cribed has been developed to interpret XRD patterns, peak detection has imp lications in every chemical application where the recognition of peak-shape d signals in analytical data is important. (C) 2000 Elsevier Science B.V. A ll rights reserved.