Supercritical fluid extraction of raspberry seed oil: Experiments and modelling

Abstract

The aim of this study was the optimization of supercritical fluid extraction (SFE) of raspberry seed oil. Sequential extraction kinetic modelling and artificial neural networks (ANN) were used for this purpose. SFE was performed according to the broaden Box-Behnken experimental design with pressure, temperature and CO2 flow rate as independent variables, while the influence of particle size on extraction kinetics and adjustable model parameters was additionally evaluated. Five empirical kinetic equations and mass-transfer model proposed by Sovová were utilized for extraction kinetics modelling. According to appropriate statistical parameters (R2, SSE and AARD), the mass-transfer model exhibited the best fit of experimental data. The initial mass-transfer rate of extraction curve was used as a response variable in ANN optimization. SFE should be performed at elevated pressure and CO2 flow rate, while temperature and particle size should be held at a lower level in order to achieve a maximal initial mass-transfer rate.