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 (X₁: 0.5-1 mm), methanol concentration (X₂: 60-70% in distilled water), ultrasound exposure time (X₃: 18-28 min), temperature (X₄: 35-45 °C), and ultrasound intensity (X₅: 65-75 W cm^-2) at five levels (- 2, - 1, 0, + 1, and + 2) concerning dependent variables, total phenolic content (y₁; TPC), tota

SUBMITTER: Kunjiappan S 

PROVIDER: S-EPMC10786918 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature