| Summary: | In a completely green approach to the exploitation of kiwi juice pomace (KP), a microwaved-assisted extraction (MAE) process was performed to extract antioxidant compounds present in KP, evaluating the influence of four independent process variables (temperature (T), extraction time (E), solvent composition (C), and solid-to-solvent ratio (R)) on the response of total phenolic content (TPC). The optimal conditions for the green extraction of total polyphenols from KP were obtained using a three-level fractional factorial design under response surface methodology (RSM) coupled with desirability optimization, and a feed-forward multilayered perceptron artificial neural network (ANN) with a back-propagation algorithm. Data were analyzed by ANOVA and fitted to a second-order polynomial equation using the regression method. Results showed that T was the most influential factor, followed by R and C, whereas the extraction time (E) was not shown to have a significant linear effect on the extraction yield of total polyphenols (TPs). The optimal conditions based on both individual and combinations of all responses were found out (T: 75 °C; E: 15 min; C: 50% ethanol:water; R: 1:15), and under these conditions the obtained extract showed both a high bioactive compound content and a high antioxidant potential, pointing out how this by-product could become an inexpensive source of compounds with high added value. A very good agreement was observed between experimental and calculated extraction yields, thus supporting the use of these models to quantitatively describe the recovery of natural antioxidants from KP. Finally, the ANN model exhibited more accurate prediction and better generalization capabilities than the RSM model (R<sup>2</sup>: 0.90 and 0.99, for RSM and ANN, respectively).
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