Optimisation of heat reflux extraction of gallic acid, quercetin and chlorogenic acid from <i>Stevia rebaudiana</i> (Bertoni) leaves by using response surface methodology and adaptive neuro-fuzzy inference system modelling

<i>Stevia rebaudiana</i> Bertoni, belongs to the <i>Asteraceae</i> family is a perennial shrub and widely recognised as a natural sweetener. This plant contains steviol glycosides and antioxidant-rich phytoconstituents. However, optimisation of extraction parameters of antioxidants from this plant are not fully explored yet. The present study aimed to optimise heat-reflux extraction (HRE) using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM). The extraction efficiency was evaluated in terms of antioxidant activity and the yield of gallic acid, quercetin, and chlorogenic acid. Plackett-Burman design (PBD) modelling was used significant parameters followed by Box-Behnken Design (BBD). Quantification of gallic acid, quercetin, and chlorogenic acid was performed using High-Performance Thin-Layer Chromatography (HPTLC) with optimised mobile phases-hexane:ethyl acetate:acetone (16.4:3.6:0.2 v/v), ethyl acetate:glacial acetic acid:formic acid:water (20:2.2:2.2:5.2 v/v), and toluene:ethyl acetate:formic acid (13.5:9:0.6 v/v), respectively. Optimal extraction conditions were established and ANFIS predictions exhibited strong concordance with experimental and RSM outcomes.