Extraction of Epigallocatechin-3-gallate from green tea via supercritical fluid technology: Neural network modeling and response surface optimization

In this study the extraction of (−)-Epigallocatechin-3-gallate (EGCG) from Iranian green tea was investigated by supercritical CO2 with ethanol as co-solvent. Design of experiments and modeling were carried out with response surface methodology by Minitab software. The HPLC analysis of the extracted samples was used in conjunction with response surface design to optimize four operating variables of supercritical CO2 extraction (pressure, temperature, CO2 flow rate and extraction dynamic time). Optimum recovery of EGCG (0.462 g/g) was obtained at 19.3 MPa, 43.7 °C, 106 min (dynamic) and 1.5 ml/min (CO2 flow rate). Moreover, a three-layer artificial neural network was developed for modeling EGCG extraction from green tea. In this regard, different networks (by changing the number of neurons in the hidden layer and algorithm of network training) were compared with evaluation of networks accuracy in extraction recovery prediction. Finally, the Levenberg–Marquardt algorithm with the six neurons in the hidden layer has been found to be the most suitable network.

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► RSM and ANN were suitable for modeling of EGCG extraction.
► Optimal ANN was determined to be MLP network with six neurons in hidden layer.
► Optimum recovery was obtained at 19.3 MPa, 43.7 °C, 106 min and 1.5 ml/min via RSM.