Optimization of ultrasound assisted extraction of polyphenols from Meghalayan cherry fruit (Prunus nepalensis) using response surface methodology (RSM) and artificial neural network (ANN) approach

Abstract

In this work, polyphenols have been recovered from Meghalayan cherry using ultrasound assisted extraction (UAE). The RSM and ANN were used to analyse the effect of solvent concentration (40–80%), solvent-solid ratio (10–30 ml/g), extraction time (15–45 min) and amplitude (20–40%) on yield and total phenol content (TPC) of fruit extract. The yield (41.30%), TPC (117.80 mgGAE/g), DPPH (85.11 ± 0.40%) and FRAP (0.92 ± 0.46 mg AAE/g) of Meghalayan cherry extracts were obtained under optimal extraction conditions (solvent concentration (58%); solvent-solid ratio (26 ml/g); time (31 min); amplitude (28%)). Results indicated that RSM and ANN could be effectively applied to describe the experimental data, but ANN showed better prediction accuracy as compared to RSM due to higher R2 and lesser MSE. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the presence of polyphenols and Liquid chromatography mass spectroscopy quadrupole time-of-flight (LC-MS-Q-TOF) identified sixteen polyphenols compounds, out of which ten were proposed for the first time in Meghalayan cherry fruit. Scanning electron microscopy showed that ultrasound processing caused disruptions in the cell wall of fruit powder which leads to higher extraction of polyphenolic compounds. Thus, the extract obtained from UAE has higher antioxidant activity and would be used in pharmaceuticals and food products as a source of bioactive compounds. Furthermore, ANN found to be an efficient tool for predicting extraction efficiency of Meghalayan cherry polyphenols.