Influence of extraction techniques on biologically enriched raw soursop fruit and comparative evaluation by response surface methodology and artificial neural network

Abstract

This work involved optimization of best extraction condition and correlation between response surface methodology (RSM) and artificial neural network (ANN), based on the bioactive compounds extracted from raw soursop fruit. The soursop fruit powder was extracted by Soxhletextraction (SOX), ultrasound-assisted extraction (UAE), solvent extraction (SVE), and microwave assisted extraction (MAE). SVE had highest antioxidant activity and biological compounds (total phenolic content of 31.12 mg GAE/g and flavonoid content of 61.442 mg QE/g). Functional groups present in the extracted samples were assured by Fourier transform infrared (FTIR) spectroscopy. RSM was used to estimate optimal extraction parameters in SVE. Sample to solvent ratio, time, and temperature were considered as independent variables and the extraction performed at 1:31 ratio, 49 hrs and 37 °C revealed superior biological activity. The experimental data were also modeled by ANN and comparative analysis between ANN and RSM concluded that the ANN was more reliable, since better statistical parameters have been observed. The liquid chromatography-mass spectrometry (LC-MS) analysis showed the presence of phenolics and acetogenin. Therefore, this optimized extract of raw soursop fruit could be helpful for food and pharmaceutical organizations.