Abstract
The FT-Raman quantification of atorvastatin calcium in tablets was performed using the partial least squares (PLS), principal component regression (PCR) and counter-propagation artificial neural networks (CP-ANN) methods. To compare the predictive abilities of the elaborated models, the relative standard errors of prediction (RSEP) were calculated. The application of PLS, PCR and 6 × 6 CP-ANN provided models of comparable quality. RSEP error values in the range of 1.9–2.8% for calibration and validation data sets were obtained for the three procedures applied. Four commercial products containing 10, 20 or 40 mg of atorvastatin calcium per tablet were successfully quantified. Concentrations found from the Raman data analysis correlate strongly with the declared values, with a recovery of 98.5–101.3%, and with the results of reference analysis, with the recovery of 98.9–102.1%, for the different models. The proposed procedure can be a fast, precise and convenient method of atorvastatin calcium quantification in commercial tablets.
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