Pure component contribution algorithm (PCCA) is an accurate analytical method was developed for extracting the contribution of each component from a mixture where the components of this mixture are completely overlapped. It is based on elimination the signal of interfering components using mean centering as a processing tool finally, the pure contribution of each component is extracted form complex signals and that worked as a finger-print resolution for the component. (PCCA) has been successfully applied to UV data of ciprofloxacin hydrochloride (CIP) in the presence of its acidic degradation product (DCIP). The spectrophotometric technique (PCCA) was successfully applied to the laboratory prepared mixtures and the pharmaceutical dosage form where the purity of the extracted signals were tested by calculating the spectral contrast angle (O) where the results were compared to show the capability to recover pure spectral profiles and detect the presence of impurities. The proposed method proved that spectrophotometric techniques can be used for identification and separation of signals, similar to chromatographic techniques. The method validated according to the ICH guidelines and the accuracy, precision and repeatability found to be within the acceptable limits. The selectivity of the proposed method was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of ciprofloxacin in quality-control laboratories.
Cite this article:
Amir Alhaj Sakur, Reem Hasan Obaydo. PCCA Algorithm as a fingerprint resolution technique for the analysis of Ciprofloxacin in the presence of its acid induced degradation product. Research J. Pharm. and Tech. 2020; 13(12):5999-6006. doi: 10.5958/0974-360X.2020.01046.X
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