ANN and RSM based Modeling of Moringa Stenopetala Seed Oil Extraction: Process Optimization and Oil Characterization

Abstract

Moringa Stenopetala is a plant species that is endemic to the southern region of Ethiopia. It is primarily cultivated for its nutritional value and is considered an important food source. The present research aimed to analyse the physicochemical properties of Moringa Stenopetala seed oil (MSO) obtained through solvent extraction method utilising hexane as the solvent. The collection of seeds was conducted in Adama, which is situated in the East Shawa zone of Oromia, Ethiopia. Prior to the extraction procedure, the seeds' average moisture content, crude ash, fibre, protein, and oil content were analysed and found to be 6.27%, 7.8%, 2.7%, 26.5%, and 43.2%, respectively. Using the Response Surface Method (RSM) and Artificial Neural Network (ANN), the extraction process was modeled. The study utilised numerical solutions to determine the optimal process parameters for maximising oil yield during extraction. The results indicated that a particle size of 0.85mm, a temperature of 85°C, and an extraction time of 4.75 hours were the most effective parameters. Furthermore, an investigation was conducted on the physical and chemical properties of the oil obtained under optimised conditions.