Solid state assay of ranitidine HCl as a bulk drug and as active ingredient in tablets using DRIFT spectroscopy with artificial neural networks

Purpose. A new, simple, sensitive and rapid method was developed to analyse the polymorphic purity of crystalline ranitidine-HCl as a bulk drug and from a tablet formulation. Methods. Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy was combined with Artificial Neural Networks (ANNs) as a data modelling tool. A standard feed-forward network, with backpropagation rule and with single hidden layer architecture was chosen. Reduction and transformation of the spectral data enhanced the ANN performance and reduced the complexity of the ANNs model. Spectral intensities from 1738 wavenumbers were reduced into 173 averaged spectral values. These 173 values were used as inputs for the ANN. Following a sensitivity analysis the number of inputs was reduced to 30, or 35, these being the input windows which had most effect on the output of the ANN. Results. For the bulk drug assay, the ANN model had 30 inputs selected from a sensitivity analysis, one hidden layer, and two output neurons, one for the percentage of each ranitidine hydrochloride crystal form. The model could simultaneously distinguish between crystal forms and quantify them enabling the physical purity of the bulk drug to be checked. For the tablet assay, the ANN model had 173 averaged spectral values as the inputs, one hidden layer and five output neurons, two for the percentage of the two ranitidine hydrochloride crystal forms and three more outputs for tablet excipients and additives. The ANN was able to solve the problem of overlapping peaks and it successfully identified and quantified all components in tablet formulation with reasonable accuracy. Conclusions. Some of the advantages over conventional analytical methods include simplicity, speed and good selectivity. The results from DRIFT spectral quantification study show the benefits of the neural network approach in analysing spectral data.