Yehia A.M.Elbalkiny H.T.Riad S.M.Elsaharty Y.S.Analytical Chemistry DepartmentFaculty of PharmacyCairo UniversityKasr-El Aini 13 StreetCairo11562Egypt; Chemistry DepartmentSchool of Pharmacy and Pharmaceutical IndustriesBadr University in CairoBadr CityCairo 11829Egypt; Analytical Chemistry DepartmentFaculty of PharmacyOctober University for Modern Sciences and Arts (MSA)6th of October City11787Egypt2020-01-092020-01-09201913861425https://doi.org/10.1016/j.saa.2019.04.081PubMed ID 31063958https://t.ly/dObOLScopusChemometrics 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. � 2019EnglishOctober University for Modern Sciences and ArtsUniversity for Modern Sciences and ArtsMSA Universityجامعة أكتوبر للعلوم الحديثة والآدابArtificial neural networksCephalosporinsMultivariate Curve Resolution-Alternating Least SquaresWater samplesAntibioticsChromatographic analysisChromatographyLeast squares approximationsNeural networksAnalytical performanceCephalosporinsChromatographic methodsMultivariate curve resolution alternating least-squaresOverlapped spectraPartial least square (PLS)Principal component regressionWater samplesData handlingantiinfective agentcephalosporin derivativeartificial neural networkleast square analysismultivariate analysisproceduresspectrophotometrywaste waterwater pollutantAnti-Bacterial AgentsCephalosporinsLeast-Squares AnalysisMultivariate AnalysisNeural Networks (Computer)SpectrophotometryWaste WaterWater Pollutants, ChemicalArticlehttps://doi.org/10.1016/j.saa.2019.04.081PubMed ID 31063958