Two multivariate calibration models for assay of niacin in complex mixtures with its official impurities: A pharmaceutical application

Ibrahim A. Naguib, Fatma F. Abdallah

Research output: Contribution to journalJournal articlepeer-review

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Background: Niacin (NIA) is a water-soluble vitamin and the primary treatment of pellagra. No analytical method was found to assess NIA in complex mixtures with its official impurities. Objective: Two validated, accurate, and selective chemometric models were developed to assay NIA in the presence of its four official impurities, including pyridine, a nephrotoxic and hepatotoxic substance. Additionally, the two selective chemometric models were compared by processing UV spectra in the range 220–305 nm and applying partial least squares regression (PLSR) and support vector regression (SVR) models. Method: A five levels five factors experimental design was chosen to exhibit a training set of 25 mixtures that had numerous variable percentages of tested substances. A test set consisting of 10 mixtures was designed to confirm the predictive power of the suggested models. Results: The presented results substantiate the strength of the developed multivariate calibration models to assay NIA specifically with high selectivity and accuracy (100.02 6 1.312 and 100.04 6 1.272 for PLSR and SVR models, respectively). The root mean square error of prediction for the validation set mixtures was applied as a main comparison tool and it was found to be 0.2016 and 0.1890 for PLSR and SVR models, respectively. Conclusions: The results of the developed models and the reported HPLC method were statistically compared, where F-values and Student’s t-tests did not show significant difference in regards to accuracy and precision. The SVR model proved to be more accurate than the PLSR model, producing a high generalization capacity, while PLSR was easy to implement and fast. Highlights: Assay of Niacin with its official impurities. Comparative study of PLSR and SVR models. Coupling of UV data and chemometrics for pharmaceutical analysis.

Original languageEnglish
Pages (from-to)1660-1666
Number of pages7
JournalJournal of AOAC International
Issue number6
StatePublished - 2021


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