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Development and validation of UV spectrophotometric method for estimation of naringenin in phytosomal formulation: interlaboratory comparison, capability, and statistical analysis

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Abstract

The goal of the current work was to establish a simple, reproducible, and reliable UV spectrophotometric method for determining naringenin in phytosomal formulations. The solvent methanol was optimized and spectrophotometric analysis was carried out at 289 nm. The proposed method was also validated for linearity, specificity, accuracy, precision, ruggedness, and robustness in accordance with the International Council for Harmonisation (ICH). With a correlation coefficient of 0.9982, the concentration of naringenin in the range of 2–14 μg/ml follows Lambert–Beer rule. The accuracy ranged between 99.33 and 99.75%. The predicted percent recovery was found to be 99.48 ± 0.41. It was found that the limits of detection (LOD) and quantification were 0.54 μg/ml and 1.66 μg/ml, respectively. The normality of the data is accepted by the Shapiro–Wilk test (P = 0.9065) and Shapiro–Francia test (W = 0.9866). On the Bland–Altman plot, an acceptable repeatability coefficient was found. The remarkable inter-laboratory repeatability was proved by the Youden plot, which was also utilized to spot random and total errors. Levey–Jennings charts and other control diagrams have demonstrated that the method is statistically controlled. Greater values of Cp (1.03) and Cpk (0.83) were seen in the capability analysis, indicating that the method could analyze the samples reliably and consistently with minimum variation. The validation report demonstrated that the proposed method was appropriate for naringenin detection and analysis in phytosomal formulations, indicating that it can be employed for routine laboratory analysis as well.

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Correspondence to Vijay Metkari.

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Metkari, V., Shah, R. & Salunkhe, N. Development and validation of UV spectrophotometric method for estimation of naringenin in phytosomal formulation: interlaboratory comparison, capability, and statistical analysis. ANAL. SCI. 39, 1917–1928 (2023). https://doi.org/10.1007/s44211-023-00401-w

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