Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.

Spectral Data Analysis for a Complex Drug Mixture Containing Altizide, Potassium Canrenoate, and Rescinnamine

M. De Luca
;
G. Ioele;G. Ragno
2021

Abstract

Conventional and chemometric spectrophotometric techniques were compared for their analytical performance in determining a tri-component pharmaceutical mixture containing altizide, potassium canrenoate, and rescinnamine. These components were characterized by a notable spectral overlap, thus making their quantitative determination particularly difficult. The determination of altizide and canrenoate was performed using the technique of different order-derivative spectrophotometry, while rescinnamine was determined by fluorometry with activation and fluorescence maxima respectively at 325 and 427 nm thanks to a total absence of interference from the other two components. The analysis of the mixture was carried out by applying multivariate calibration methods, including principal component (PCR) and partial least squares (PLS) regression approaches. The calibration sample set was defined by a simplex-lattice experimental design to cover the experimental domain distributed over five concentration levels. The prediction accuracy of the defined methods was evaluated through external validation on new unknown samples and commercial pharmaceuticals. Significant advantages were found in the prediction of all the analytes when using the chemometric methods, which proved to be simpler, faster, and showing better statistical results with accuracy values between 96.12 and 103.36% and relative standard errors lower than 1.7%.
Drug analysis, derivative spectrophotometry, fluorometry, principal component regression, partial least squares regression.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/310249
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