Modeling and optimization of microwave-assisted extraction of total phenolics content from mango (Mangifera indica) peel using response surface methodology (RSM) and artificial neural networks (ANN)

Abstract

Mango (Mangifera indica) is a fruit highly consumed for its flavor and nutrient content. The mango peel is rich in compounds with biological functionality, such as antioxidant activity among others. The influence of microwave-assisted extraction variables on total phenol compounds (TPC) and antioxidant activity (TEAC) of natural extracts obtained from mango peel var. Tommy and Sugar were studied using a response surface methodology (RSM) and Artificial Neural Networks (ANN). TPC of mango peel extract var. Tommy was significantly influenced by time extraction (X1), solvent/plant ratio (X2) and concentration of ethanol (X3) and while mango peel extract var. Sugar was influenced by X2. TEAC by ABTS was significantly influenced by X3. Maximum of TPC (121.3 mg GAE / g of extract) and TEAC (1185.9 μmol Trolox/g extract) for mango peel extract var. Tommy were obtained at X1=23.9s, X2=12.6mL/gand X3=63.2%, and for mango peel extract var. Sugar, the maximum content of TPC (224.86 mg GAE/g extract) and TEAC (2117.7 μmol Trolox/g extract) were obtained at X1=40s, X2=10mL/g and X3=74.9%. The ANN model presented a higher predictive capacity than the RSM (RANN2>RRSM2,RMSEANN<RMSERSMandAADANN<AADRSM). These extracts can be used for the development of new products.