{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T01:32:43Z","timestamp":1786325563434,"version":"build-2736575974"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T00:00:00Z","timestamp":1556150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>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\u2014hexane, chloroform, and acetone\u2014with 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 \u00d7 10\u22123 to 3.70 \u00d7 10\u22127), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.<\/jats:p>","DOI":"10.3390\/foods8040142","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T10:44:06Z","timestamp":1556189046000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multiple Modeling Techniques for Assessing Sesame Oil Extraction under Various Operating Conditions and Solvents"],"prefix":"10.3390","volume":"8","author":[{"given":"Haitham","family":"Osman","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3357-5922","authenticated-orcid":false,"given":"Ihab","family":"Shigidi","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Arabi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"key":"ref_1","first-page":"547","article-title":"Sesame oil","volume":"2","author":"Hwang","year":"2005","journal-title":"Bailey\u2019s Ind. Oil Fat Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.jhazmat.2010.01.070","article-title":"Parameters optimization of rice husk ash (RHA)\/CaO\/CeO2 sorbent for predicting SO2\/NO sorption capacity using response surface and neural network models","volume":"178","author":"Dahlan","year":"2010","journal-title":"J. Hazard. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Elleuch, M., Bedigian, D., Zitoun, A., and Zouari, N. (2011). Sesame (Sesamum indicum L.) seeds in food, nutrition and health. Nuts Seeds Health Dis. Prev., 1029\u20131036.","DOI":"10.1016\/B978-0-12-375688-6.10122-7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3989\/gya.0997171","article-title":"Variation in oil content and fatty acid composition of sesame accessions from different origins","volume":"69","author":"Kurt","year":"2018","journal-title":"Grasas Y Aceites"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1002\/lite.201000042","article-title":"Impacts of roasting oily seeds and nuts on their extracted oils","volume":"22","author":"Durmaz","year":"2010","journal-title":"Lipid Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"305","DOI":"10.4236\/aces.2015.53031","article-title":"Optimization of sesame oil extraction process conditions","volume":"5","author":"Elkhaleefa","year":"2015","journal-title":"Adv. Chem. Eng. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jfoodeng.2010.04.011","article-title":"Extraction of sesamin from sesame oil using macroporous resin","volume":"100","author":"Zhou","year":"2010","journal-title":"J. Food Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.foodchem.2006.09.008","article-title":"Quality characteristics of sesame seeds and by-products","volume":"103","author":"Elleuch","year":"2007","journal-title":"Food Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.supflu.2009.11.012","article-title":"Extraction of sesame seed (Sesamun indicum L.) oil using compressed propane and supercritical carbon dioxide","volume":"52","author":"Corso","year":"2010","journal-title":"J. Supercrit. Fluids"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.foodchem.2010.09.064","article-title":"Aqueous enzymatic sesame oil and protein extraction","volume":"125","author":"Latif","year":"2011","journal-title":"Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.lwt.2015.08.053","article-title":"Improvements in the quality of sesame oil obtained by a green extraction method using enzymes","volume":"65","author":"Ribeiro","year":"2016","journal-title":"LWT-Food Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.foodchem.2012.04.111","article-title":"Response surface optimisation of aqueous enzymatic oil extraction from bayberry (Myrica rubra) kernels","volume":"135","author":"Zhang","year":"2012","journal-title":"Food Chem."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Casas, M.P., and Gonz\u00e1lez, H.D. (2017). Enzyme-assisted aqueous extraction processes. Water Extraction of Bioactive Compounds, Elsevier.","DOI":"10.1016\/B978-0-12-809380-1.00013-9"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"463","DOI":"10.3989\/gya.132212","article-title":"Enzyme-aided cold pressing of flaxseed (Linum usitatissimum L.): Enhancement in yield, quality and phenolics of the oil","volume":"64","author":"Anwar","year":"2013","journal-title":"Grasas Y Aceites"},{"key":"ref_15","first-page":"1","article-title":"Parameters Optimization, Modelling and Kinetics of Balanites Aegyptiaca Kernel oil extraction","volume":"5","author":"Shigidi","year":"2015","journal-title":"Int. J. Chem. Eng. Appl. Sci."},{"key":"ref_16","first-page":"335","article-title":"Optimization Of Sesame Seeds Oil Extraction Operating Conditions Using The Response Surface Design Methodology","volume":"17","author":"Osman","year":"2016","journal-title":"Sci. Study Res. Chem. Chem. Eng. Biotechnol. Food Ind."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ultsonch.2014.10.019","article-title":"Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil","volume":"23","author":"Maran","year":"2015","journal-title":"Ultrason. Sonochem."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Moghaddam, M.G., and Khajeh, M. (2011). Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles. Food Nutr. Sci., 2.","DOI":"10.4236\/fns.2011.28110"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10845-014-0879-6","article-title":"Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results","volume":"27","author":"Bakhtiari","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_20","first-page":"543","article-title":"Modelling and optimization of homogenous photo-fenton degradation of rhodamine B by response surface methodology and artificial neural network","volume":"10","author":"Speck","year":"2016","journal-title":"Int. J. Environ. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"313","DOI":"10.15255\/CABEQ.2016.867","article-title":"Optimizing Model Base Predictive Control for Combustion Boiler Process at High Model Uncertainty","volume":"31","author":"Osman","year":"2017","journal-title":"Chem. Biochem. Eng. Q."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Osman, H. (2014, January 22\u201325). LQ evolution algorithm optimizer for model predictive control at model uncertainty. Proceedings of the 2014 14th International Conference Control, Automation and Systems (ICCAS), Seoul, Korea.","DOI":"10.1109\/ICCAS.2014.6987752"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989). Design and analysis of computer experiments. Stat. Sci., 409\u2013423.","DOI":"10.1214\/ss\/1177012413"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1951.tb00067.x","article-title":"On the experimental attainment of optimum conditions","volume":"13","author":"Box","year":"1951","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1029\/JB076i008p01905","article-title":"Multiquadric equations of topography and other irregular surfaces","volume":"76","author":"Hardy","year":"1971","journal-title":"J. Geophys. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Burges, C., and Vapnik, V. (1996, January 16\u201319). Incorporating invariances in support vector learning machines. Proceedings of the International Conference on Artificial Neural Networks, Bochum, Germany.","DOI":"10.1007\/3-540-61510-5_12"},{"key":"ref_27","unstructured":"Haykin, S. (1994). Neural Networks: A Comprehensive Foundation, Prentice Hall PTR."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.2514\/2.1234","article-title":"Kriging models for global approximation in simulation-based multidisciplinary design optimization","volume":"39","author":"Simpson","year":"2001","journal-title":"AIAA J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.paerosci.2008.11.001","article-title":"Recent advances in surrogate-based optimization","volume":"45","author":"Forrester","year":"2009","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1007\/s00158-016-1569-0","article-title":"Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias","volume":"55","author":"Amouzgar","year":"2017","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1016\/j.compstruc.2005.02.025","article-title":"A comparative study of metamodeling methods for multiobjective crashworthiness optimization","volume":"83","author":"Fang","year":"2005","journal-title":"Comput. Struct."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s00158-003-0353-0","article-title":"Comparison of global and local response surface techniques in reliability-based optimization of composite structures","volume":"26","author":"Singh","year":"2004","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1080\/03052150108940940","article-title":"Adaptive response surface method-a global optimization scheme for approximation-based design problems","volume":"33","author":"Wang","year":"2001","journal-title":"Eng. Optim."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s00158-002-0224-0","article-title":"Managing approximation models in multiobjective optimization","volume":"24","author":"Yang","year":"2002","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"143","DOI":"10.20450\/mjcce.2017.991","article-title":"Response surface methodology approach for optimization of methyl orange adsorptive removal by magnetic chitosan nanocomposite","volume":"36","author":"Ayati","year":"2017","journal-title":"Maced. J. Chem. Chem. Eng."},{"key":"ref_36","first-page":"1573","article-title":"Comparative assessment of response surface methodology quadratic models and artificial neural network method for dry reforming of natural gas","volume":"40","author":"Gendy","year":"2018","journal-title":"Energy Sourcespart A Recoveryutilizationand Environ. Effect"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0377-0427(98)00218-0","article-title":"On the smoothness of positive definite and radial functions","volume":"101","author":"Wendland","year":"1999","journal-title":"J. Comput. Appl. Math."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Krishnamurthy, T. (2003, January 9). Response surface approximation with augmented and compactly supported radial basis functions. Proceedings of the 44th AIAA\/ASME\/ASCE\/AHS\/ASC Structures, Structural Dynamics, and Materials Conference, Norfolk, VA, USA.","DOI":"10.2514\/6.2003-1748"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1533\/cras.2000.0121","article-title":"Development of a detailed vehicle finite element model part I: Methodology","volume":"5","author":"Zaouk","year":"2000","journal-title":"Int. J. Crashworthiness"}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/8\/4\/142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:47:08Z","timestamp":1760186828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/8\/4\/142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,25]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["foods8040142"],"URL":"https:\/\/doi.org\/10.3390\/foods8040142","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,25]]}}}