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Open AccessArticle

Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents

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Department of Chemical Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia
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Department of Mechanical Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia
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Author to whom correspondence should be addressed.
Foods 2019, 8(4), 142; https://doi.org/10.3390/foods8040142
Received: 16 January 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 25 April 2019
This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents—hexane, chloroform, and acetone—with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10−3 to 3.70 × 10−7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models. View Full-Text
Keywords: sesame oil extraction; response surface method; radial basis functions; artificial neural network sesame oil extraction; response surface method; radial basis functions; artificial neural network
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MDPI and ACS Style

Osman, H.; Shigidi, I.; Arabi, A. Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents. Foods 2019, 8, 142. https://doi.org/10.3390/foods8040142

AMA Style

Osman H, Shigidi I, Arabi A. Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents. Foods. 2019; 8(4):142. https://doi.org/10.3390/foods8040142

Chicago/Turabian Style

Osman, Haitham; Shigidi, Ihab; Arabi, Amir. 2019. "Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents" Foods 8, no. 4: 142. https://doi.org/10.3390/foods8040142

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