Yellow Oleander Seed Oil Extraction Modeling and Process Parameters Optimization: Performance Evaluation of Artificial Neural Network and Response Surface Methodology
Journal of Food Processing and Preservation
Chemical Engineering Department, Obafemi Awolowo University, OAU Campus, Ile-Ife, Osun State, Nigeria
The effects of sample weight, time and solvent type on YOSO yield were evaluated using ANN and RSM. The predicted optimal condition for the extraction process was found to be the same for the ANN and RSM models developed: sample weight of 20g, time of 3h and petroleum ether. The models predictions of YOSO yield (ANN [77.42%] and RSM [78.64%]) at optimum levels were verified experimentally (ANN [77.63%] and RSM [76.64%]). Evaluation of the models by R2 and AAD showed that the ANN model was better (R2=1.00, AAD=0.61%) than the RSM model (R2=0.98, AAD=3.19%) in predicting YOSO yield. Physicochemical properties of the YOSO indicated that it was nonedible and the fatty acids profile showed that the oil was highly unsaturated (76.13%). Practical Applications: This study demonstrated modeling of YOSO extraction and optimization of process parameters that are involved. The performance evaluation results showed that both the ANN and RSM could be used for modeling and optimization of YOSO extraction process. Also, the characterization of the oil showed that it could serve as raw material for many chemical industries such as biodiesel production, soap, cosmetic and pharmaceutical industrials. The results from this study can be successfully scaled up to pilot scale. Also, the results could be extended to the extraction of other oilseeds. © 2015 Wiley Periodicals, Inc.
Chemical industry; Fatty acids; Neural networks; Oils and fats; Unsaturated fatty acids; Biodiesel production; Extraction process; Fatty acids profiles; Modeling and optimization; Optimization of process parameters; Physicochemical property; Process parameters optimizations; Response surface methodology; Extraction