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

Abstract

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 1: 0.5–1 mm), methanol concentration (X 2: 60–70% in distilled water), ultrasound exposure time (X 3: 18–28 min), temperature (X 4: 35–45 °C), and ultrasound intensity (X 5: 65–75 W cm−2) at five levels (− 2, − 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y1; TPC), total flavonoid content (y2; TFC), 2, 2-diphenyl-1-picrylhydrazyl free radicals scavenging (y3; %DPPH*sc), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) free radicals scavenging (y4; %ABTS*sc) and Ferric ion reducing antioxidant potential (y5; FRAP) were selected. The optimized condition was observed at X 1 = 0.155 mm, X 2 = 65% methanol in water, X 3 = 23 min ultrasound exposure time, X 4 = 40 °C, and X 5 = 70 W cm−2 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.