Chemometrics for resolving spectral data of cephalosporines and tracing their residue in waste water samples
Yehia A.M.; Elbalkiny H.T.; Riad S.M.; Elsaharty Y.S.
Date issued:
2019
Publisher:
Elsevier B.V.
Series Info:
Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
219
Type:
Article
Keywords:
October University for Modern Sciences and Arts
,
University for Modern Sciences and Arts
,
MSA University
,
جامعة أكتوبر للعلوم الحديثة والآداب
,
Artificial neural networks
,
Cephalosporins
,
Multivariate Curve Resolution-Alternating Least Squares
,
Water samples
,
Antibiotics
,
Chromatographic analysis
,
Chromatography
,
Least squares approximations
,
Neural networks
,
Analytical performance
,
Cephalosporins
,
Chromatographic methods
,
Multivariate curve resolution alternating least-squares
,
Overlapped spectra
,
Partial least square (PLS)
,
Principal component regression
,
Water samples
,
Data handling
,
antiinfective agent
,
cephalosporin derivative
,
artificial neural network
,
least square analysis
,
multivariate analysis
,
procedures
,
spectrophotometry
,
waste water
,
water pollutant
,
Anti-Bacterial Agents
,
Cephalosporins
,
Least-Squares Analysis
,
Multivariate Analysis
,
Neural Networks (Computer)
,
Spectrophotometry
,
Waste Water
,
Water Pollutants, Chemical
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. � 2019
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