Optimization of the extraction conditions of Nypa fruticans Wurmb. using response surface methodology and artificial neural network

Abstract

In this study, we conducted response surface methodology (RSM) and artificial neural network (ANN) to predict and estimate the optimized extraction condition of Nypa fruticans Wurmb. (NF). The effect of ethanol concentration (X1; 0–100%), extraction time (X2; 6–24 h), and extraction temperature (X3; 40–60 °C) on the antioxidant potential was confirmed. The optimal conditions (57.6% ethanol, 19.0 h extraction time, and 51.3 °C extraction temperature) of 2,2-diphenyl-1-1picrylhydrazyl (DPPH) scavenging activity, cupric reducing antioxidant capacity (CUPRAC) and ferric reducing antioxidant power (FRAP), total phenolic content (TPC), and total flavonoid contents (TFC) resulted in a maximum value of 62.5%, 41.95 and 48.39 µM, 143.6 mg GAE/g, and 166.8 CAE/g, respectively. High-resolution mass spectroscopic technique was performed to profile phenolic and flavonoid compounds. Upon analyzing, total 48 compounds were identified in NF. Altogether, our findings can provide a practical approach for utilizing NF in various bioindustries.