Development and validation of different chemometric-assisted spectrophotometric methods for determination of cefoxitin-sodium in presence of its alkali-induced degradation product

Abstract

This work, introduces different powerful chemometric methods for determination of cefoxitin-sodium in presence of its alkali-induced degradation product without prior separation steps, dubbed; Principal component regression, Partial least squares with and without variable selection (Genetic Algorithm), Artificial neural network with and without variable selection (Genetic Algorithm), and Classical least square. The predictive abilities of the models were tested and the results proved that proposed methods were successfully applied for the determination of cefoxitin-sodium in its pure form and in its powder for injection. Keywords: Cefoxitin-sodium, Principal component regression, Partial least squares, Genetic algorithm, Artificial neural network, Classical least square