Chemometrics for resolving spectral data of cephalosporines and tracing their residue in waste water samples

Abstract Chemometrics approaches have been used in this work to trace cephalosporins in aquatic system. Principal component regression (PCR), partial least squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN) were compared to resolve the severally overlapped spectrum of three selected cephalosporins; cefprozil, cefradine and cefadroxil. The analytical performance of chemometric methods was compared in terms of errors. Artificial neural networks provide good recoveries with lowest error. Satisfactory results were obtained for the proposed chemometric methods whereas ANN showed better analytical performance. The qualitative meaning in MCR-ALS transformation provided very well correlations between the pure and estimated spectra of the three components. This multivariate processing of spectrophotometric data could successfully detect the studied antibiotics in waste water samples and compared favorably to alternative costly chromatographic methods.