Optimization of extraction of phenolic compounds that have high antioxidant activity from Rhodomyrtus tomentosa (Ait.) Hassk. (Sim) in Chi Linh, Hai Duong

Rose myrtle, Rhodomyrtus tomentosa, has long been used to produce beverages, cosmetics, and pharmaceutical products since its leaves and stem contain the large amounts of polyphenols which have high antioxidant capacity. In the present study, the optimal conditions for extraction of polyphenolic compounds with high antioxidant capacity from R. tomentosa were determined by nonlinear programing. From three parts of R. tomentosa asleaves, stems and fruits, leaves contained the highest total polyphenol content and antioxidant capacity (104.16±26.2 mg GAE/g DW and 1026.89±161.5µM TE/g DW). Besides, the effect of three technological factors including ethanol concentration, temperature and extraction time on the phenolic extraction yield from R. tomentosaleaves were investigated. The response surface methodology and a rotatable central composite design consisting of 21 experimental runs in triplicate at the center point were then applied to create the model describing the extraction. Three independent variables, the ethanol concentration (X1), temperature (X2), and extraction time (X3) that affect the response, the antioxidant capacity of the extracts (Y) were determined and other regression coefficients for intercept, linear, quadratic, and interaction terms were also induced, thereby the model of the polyphenol extraction was obtained as follows: Y = 3093.856 + 20.488X1 - 55.106X2 - 13.332X3 - 0.13X12 + 0.256 X22 - 0.054X32 - 0.146 X1X2- 0.020X1X3 + 0.297X2X3. The obtained model fitted well to the measured values (R2 = 0.941). Under the optimal conditions, 65% ethanol, 45oC, and for 30 min, the total polyphenol content of 76.42 mg GAE/g DW and antioxidant capacity of 1408.99 µM TE/g DWwere achieved. To validate the model, the extractions were performed with four replicates under these conditions. The experimental values were within the 95% confidence interval of the predicted values (p < 0.05).