Optimization of ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using RSM and ANFIS modeling with machine learning algorithm.

Plant materials are a rich source of polyphenolic compounds with interesting health-beneficial effects. The present study aimed to determine the optimized condition for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X: 0.5-1 mm), methanol concentration (X: 60-70% in distilled water), ultrasound exposure time (X: 18-28 min), temperature (X: 35-45 °C), and ultrasound intensity (X: 65-75 W cm) at five levels (- 2, - 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y; TPC), total flavonoid content (y; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y; %DPPH*sc), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y; %ABTS*sc) and Ferric ion reducing antioxidant potential (y; FRAP) were selected. The optimized condition was observed at X = 0.155 mm, X = 65% methanol in water, X = 23 min ultrasound exposure time, X = 40 °C, and X = 70 W cm ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) analyses were performed to find the existence of the bioactive compounds in the optimized extract.