Al-Amoudi R.H.Taylan O.Kutlu G.Can A.M.Sagdic O.Dertli E.Yilmaz M.T.20.04.20192019-04-2020.04.20192019-04-2020190308-8146https://dx.doi.org/10.1016/j.foodchem.2018.07.211https://hdl.handle.net/20.500.12403/320In this work, response surface methodology and adaptive neuro-fuzzy inference system approaches were used to predict and model effect of extraction conditions of pectin from medlar fruit (Mespilus germanica L.). The pectin extracted at optimized conditions (89 °C, 4.83 h and 4.2 pH) could be classified as high methoxyl pectin. Sugar composition analysis showed that pectin was mainly composed of D-galacturonic acid, L-arabinose, L-rhamnose, D-galactose and D-glucose. Fourier Transform Infrared Spectroscopy, RAMAN and nuclear magnetic resonance spectra confirmed molecular structure, revealing presence of D-galacturonic acid backbone. X-ray diffraction patterns revealed an amorphous structure. Differential scanning calorimetry showed endothermic (123 °C) and exothermic peaks (192 °C). Thermogravimetric analysis revealed three decomposition regions, 50–225 °C, 225–400 °C and 400–600 °C. Steady and dynamic shear analyses revealed that pectin had a pseudo-plastic behavior with storage (G?) and loss (G?) modulus increasing with increment in frequency, indicating viscoelastic structure more predominantly elastic than viscous. © 2018 Elsevier Ltdeninfo:eu-repo/semantics/closedAccessMedlar fruitMolecular, thermal and rheological characterizationPectin extraction yieldRSM and ANFIS modelingDifferential scanning calorimetryExtractionFourier transform infrared spectroscopyFruitsFuzzy neural networksFuzzy systemsNuclear magnetic resonance spectroscopyThermogravimetric analysisAdaptive neuro-fuzzy inference systemANFIS modelExtraction yieldHigh methoxyl pectinsMedlar fruitsResponse surface methodologyRheological characterizationThermal and rheological propertiesFuzzy inferencearabinosegalactosegalacturonic acidglucosepectinrhamnoseArticlechemical structurecontrolled studydecompositiondifferential scanning calorimetryflow kineticsFourier transform infrared spectroscopyfruitfuzzy systemmeasurement repeatabilityMespilus germanicanuclear magnetic resonancephysical chemistryprocess controlRaman spectrometryresponse surface methodthermogravimetryMedlar fruitMolecular, thermal and rheological characterizationPectin extraction yieldRSM and ANFIS modelingDifferential scanning calorimetryExtractionFourier transform infrared spectroscopyFruitsFuzzy neural networksFuzzy systemsNuclear magnetic resonance spectroscopyThermogravimetric analysisAdaptive neuro-fuzzy inference systemANFIS modelExtraction yieldHigh methoxyl pectinsMedlar fruitsResponse surface methodologyRheological characterizationThermal and rheological propertiesFuzzy inferencearabinosegalactosegalacturonic acidglucosepectinrhamnoseArticlechemical structurecontrolled studydecompositiondifferential scanning calorimetryflow kineticsFourier transform infrared spectroscopyfruitfuzzy systemmeasurement repeatabilityMespilus germanicanuclear magnetic resonancephysical chemistryprocess controlRaman spectrometryresponse surface methodthermogravimetryArticle27165066210.1016/j.foodchem.2018.07.211302367282-s2.0-85051051781Q1WOS:000444967800082Q1