Cigeroglu, ZeynepAras, ÖmürPinto, Carlos A.Bayramoglu, MahmutKirbaslar, SismailLorenzo, Jose M.2021-03-202021-03-2020180022-51421097-0010http://doi.org/10.1002/jsfa.8987https://hdl.handle.net/20.500.12885/777BACKGROUND: The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 degrees C), solid:liquid ratio (50 - 100 gL(-1)) and calorimetric energy density of ultrasound (0.25-0.50 kW L-1), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor. RESULTS: The optimized experimental conditions yielded by RSM were: 10.80% for ethanol concentration; 58.52 min for extraction time; 30.37 degrees C for extraction temperature; 52.33 g L-1 for solid:liquid ratio; 0.457 kW L-1 for ultrasonic power density, with thick probe type. Under these conditions total phenolics content was found to be 19.04 mg gallic acid equivalents g dried leaf. CONCLUSION: The same dataset was used to train multilayer feed-forward networks using different approaches via MATLAB, with ANN exhibiting superior performance to RSM (differences included categorical factor in one model and higher regression coefficients), while close values were obtained for the extraction variables under study, except for ethanol concentration and extraction time. (C) 2018 Society of Chemical Industryeninfo:eu-repo/semantics/closedAccessultrasound-assisted extractionpolyphenolsgrapefruit leavesD-optimal designartificial neural networkoptimizationArticle10.1002/jsfa.8987981245844596WOS:00044030290002529508393Q1Q1