An integrated neural-fuzzy methodology for characterisation and modelling of exopolysaccharide (EPS) production levels of Leuconostoc mesenteroides DL1

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dc.authoridKAYA, Yasemin/0000-0003-2760-8959
dc.authoridTaylan, Osman/0000-0002-5806-3237
dc.contributor.authorKabli, Mohammad
dc.contributor.authorYilmaz, Mustafa Tahsin
dc.contributor.authorTaylan, Osman
dc.contributor.authorKaya, Yasemin
dc.contributor.authorIspirli, Humeyra
dc.contributor.authorBasahel, Abdulrahman
dc.contributor.authorSagdic, Osman
dc.date.accessioned2024-10-04T18:48:12Z
dc.date.available2024-10-04T18:48:12Z
dc.date.issued2020
dc.departmentBayburt Üniversitesien_US
dc.description.abstractOptimisation of exopolysaccharides (EPS) production in Lactic Acid Bacteria (LAB) is an important task as EPS production can be affected by different parameters. In this respect, this study aimed to characterise the structure of an EPS from Leuconstoc mesenteroides DL1 strain and to optimise the EPS production by determination of the effects of incubation time, sucrose concentration, incubation temperature and initial levan concentration (input parameters) using integrated ANNs (Artificial neural networks) and fuzzy modelling approaches. The characterisation of the EPS monomeric composition by HPLC analysis revealed that EPS DL1 was composed of glucose and fructose. The H-1 and C-13 NMR spectra of EPS DL1 also confirmed the glucan and fructan production. The effects of the input parameters on glucan and fructan production levels as output parameters by DL1 were optimised using neural network and fuzzy modelling tools. The fuzzy model was developed based on the recognition of basic elements of input-output parameters, and the power of ANNs used for system identification. A structural analysis was carried out to improve the flexibility of fuzzy model, and to design the unknown mappings of the input and output parameters more robustly. The parameters then were fine-tuned by qualitative reasoning to establish the relations of input output parameters using membership functions (MFs) and their intervals determination. A hybrid training algorithm was employed for parameter identification, MFs and their interval determination to obtain the fuzzy model. The model can predict the outcome parameters; glucan and fructan with high accuracy for the predetermined input parameters.en_US
dc.description.sponsorshipDeanship of Scientific Research (DSR), King Abdulaziz University, Jeddah [135 -197 D1439]; DSRen_US
dc.description.sponsorshipThis work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (135 -197 D1439). The authors, therefore, gratefully acknowledge the DSR technical and financial support.en_US
dc.identifier.doi10.1016/j.cie.2020.106619
dc.identifier.issn0360-8352
dc.identifier.issn1879-0550
dc.identifier.scopus2-s2.0-85089225619en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.cie.2020.106619
dc.identifier.urihttp://hdl.handle.net/20.500.12403/2967
dc.identifier.volume148en_US
dc.identifier.wosWOS:000574658100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Industrial Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEPS productionen_US
dc.subjectStructural characterisationen_US
dc.subjectLactic acid bacteria (LAB)en_US
dc.subjectOptimisationen_US
dc.subjectNeural networksen_US
dc.subjectFuzzy modellingen_US
dc.titleAn integrated neural-fuzzy methodology for characterisation and modelling of exopolysaccharide (EPS) production levels of Leuconostoc mesenteroides DL1en_US
dc.typeArticleen_US

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