Application of Chemometrics for Spectral Resolving and Determination of Three Analgesics in Water Samples.

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ID: 2432
2019
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Abstract
Chemometrics is a discipline that allows the spectral resolution of drugs in a complicated matrix (e.g., environmental water samples) as an alternative to chromatographic methods. Three analgesics were traced in wastewater samples with simple and cost-effective multivariate approaches using spectrophotometric data. Four chemometric approaches were applied for the simultaneous determination of diclofenac, paracetamol, and ibuprofen. Partial least squares (PLS), principal component regression (PCR), artificial neural networks (ANN), and multivariate curve resolution (MCR)-alternating least squares (ALS) were selected. The presented methods were compared and validated for their qualitative and quantitative analyses. Moreover, statistical comparison between the results obtained by the proposed methods and the official methods showed no significant differences. The proposed multivariate calibrations were accurate and specific for quantitative analysis of the studied components. MCR-ALS is the only method that has the capacity for both the quantitative and qualitative analysis of the studied drugs. Four chemometric approaches were used for analysis of severally overlapped ternary mixture of three analgesics. The analytical performance of PCR, PLS, MCR-ALS, and ANN was compared and validated in terms of root mean square error of calibration (RMSEC), SE of prediction, and recoveries. ANN gave the highest predicted concentrations with the lowest RMSEC and root mean square error of prediction. MCR-ALS has the capacity for both qualitative and quantitative measurement. The methods have been effectively applied for real samples and compared to official methods.
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yehia2019applicationjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yehia, Ali M;Elbalkiny, Heba T;Riad, Safa'a M;Elsaharty, Yasser S;
Journal journal of aoac international
Year 2019
DOI
10.5740/jaoacint.19-0140
URL
Keywords Keywords not found

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