Maximizing the extraction yield of plant gum exudate using response surface methodology and artificial neural networking and pharmacological characterization

Abstract

Prunus armeniaca gum is used as food additive and ethno medicinal purpose. Two empirical models response surface methodology and artificial neural network were used to search for optimized extraction parameters for gum extraction. A four-factor design was implemented for optimization of extraction process for maximum yield which was obtained under the optimized extraction parameter (temperature, pH, extraction time, and gum/water ratio). Micro and macro-elemental composition of gum was determined by using laser induced breakdown spectroscopy. Gum was evaluated for toxicological effect and pharmacological properties. The maximum predicted yield obtained by response surface methodology and artificial neural network was 30.44 and 30.70% which was very close to maximum experimental yield 30.23%. Laser induced breakdown spectroscopic spectra confirmed the presence Calcium, Potassium, Magnesium, Sodium, Lithium, Carbon, Hydrogen, Nitrogen and Oxygen. Acute oral toxicity study showed that gum is non-toxic up to 2000 mg/Kg body weight in rabbits, accompanied by high cytotoxic effects of gum against HepG2 and MCF-7cells by MTT assay. Overall, Aqueous solution of gum showed various pharmacological activities with significant value of antioxidant, antibacterial, anti-nociceptive, anti-cancer, anti-inflammatory and thrombolytic activities. Thus, optimization of parameters using mathematical models cans offer better prediction and estimations with enhanced pharmacological properties of extracted components.