RSM/ANN based optimized recovery of phenolics from mulberry leaves by enzyme-assisted extraction

Abstract

Recovery of phenolics from Morus alba leaves (MAL) and extraction into the solvent was optimized using enzyme-assisted extraction. The influence of four parameters, including enzyme concentration (EC), temperature (T), incubation time (t) and pH were investigated using rotatable central composite design (RCCD). Two factors, namely enzyme concentration and pH, exhibited significant effect on extraction efficacy yield of extractable phenolics from MAL. Furthermore, artificial neural network (ANN) model was executed to predict the relationship between dependent and independent variables. Among enzyme complexes (kemzyme dry-plus, natuzyme and zympex-014) employed for extraction, zympex-014 assisted extract depicted maximum amount of phenolic bioactives from MAL. Morphological changes in the cell wall of MAL residue were elucidated by scanning electron microscopy (SEM). The main phenolic compounds identified and quantified by gas chromatography mass spectrometry (GC/MS) in MAL extract were found to be quercetin, gallic acid, m-coumaric acid, cinnamic acid, syrinigc acid and vanillic acid.