Quantification of three phenolic classes and total phenolic content of propolis extracts using a single UV-vis spectrum. Issue 5 (20th October 2017) - Paula, Vanessa B; Estevinho, Leticia M; Dias, Luís G
Abstract : This work presents a methodology for simultaneously measuring the total content of three classes of phenolic compounds (hydroxybenzoic acids, hydroxycinamic acids and flavonoids), as well the total phenolic content using a single UV-vis spectrum. To test the methodology, samples of propolis were used. Firstly, an experimental design based in the Response Surface Methodology established that the best hydroethanolic solution for propolis phenolic compounds extraction had 80% of ethanol. Secondly, calibration models were developed with multiple linear regression, coupled with the leaps algorithm for variable selection, using UV-vis spectra of mixed standard solutions (gallic acid, ferulic acid and quercetin mixtures), all with orthogonal concentrations established by a multilevel fractional factorial design. The model's estimation and prediction performance had linearity between predicted and expected concentration values, with slope ranging from 0.996–1.04 and determination coefficients higher than 0.995. A quality control solution presented acceptable repeatability and accuracy (relative standard deviation percentage and percentage relative error were lower than 4.8%). Analysis of propolis phenolic compounds extracts showed acceptable precision (relative standard deviation percentages lower than 1.6%) and acceptable accuracy (recoveries assays ranged between 98 and 112%). Overall, the present multi-parametric analytical technique can be a first approach for chemical characterization of phenolic compounds extracts, such as that obtained from the propolis samples, considering its advantages of simplicity, rapidity, precision and accuracy. Moreover, this methodology can be adapted to other sample matrices, provided that the typical UV-vis absorption spectra of their classes of phenolic compounds shows partial overlapping, allowing multivariate calibration. Graphical Abstract: