Effect of Temperature, Gum Concentration and Gum Ratio on Creep-Recovery Behaviour of Carboxymethyl Cellulose -Guar Gum Mixtures: modelling with RSM and ANN
Main Article Content
Keywords
Rhelogical properties, artificial intelligence, creep and recovery test, response surface methodology
Abstract
In the present study, the effects of temperature (15, 25, 35 oC), gum concentration (1.5, 2.0 and 2.5 %) and gum ratio (25:75, 50:50, 75:25) on the creep-recovery rheological properties of carboxymethyl cellulose (CMC)-guar gum (GG) mixtures were investigated. Within the studied range of experimental design, recovery index (?J) value of CMC-GG gum solution was analyzed based on the design factors. Experimental recovery index responses were modelled using RSM (R2= 0.9711) and ANN (R2= 0.9829). Therefore, CMC-GG mixtures can be considered for use in instant beverages such as lemonade to enhance mouhtfeel and texture.
References
Augusto, P.E.D.; Ibarz, A.; Cristianini, M. Effect of high pressure homogenization (HPH) on the rheological properties of tomato juice: Creep and recovery behaviours. Food Research International 2013,54, 169-176.
Bas ? D.; Boyaci, I.H. Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. J. Food. Eng. 2007,78, 846-854.
Bayarri, S.; Dolz, M.; Hernandez, M.J. Effect of carboxymethyl cellulose concentration on rheological behavior of milk and aqueous systems. A creep and recovery study. Journal of Applied Polymer Science 2009,114,1626-1632.
Box, G.; Behnken D. Some new three level designs for the study of quantitative variables. Technometrics 1960, 2, 455-475.
Copetti, G.; Grassi, M.; Lapasin, R.; Pricl, S. Synergistic gelation of xanthan gum with locust bean gum: a rheological investigation, Glucoconjugate J.1997,14, 951-961.
Dogan, M.; Ersoz, N.B.; Toker, O.S.; Kaya, Y.; Can?y?lmaz, E. Optimization of gum combination for instant pudding based on creep and recovery parameters by mixture design approach, European Food Research and technology 2014, 238, 47-58.
Dolz, M.; Hernandez, M.J.; Delegido, J. Creep and recovery experimental investigation of low oil content food emulsions, Food Hydrolcolloid 2008, 22, 421-427.
Garci-Abuin, A.; Gomez-Diaz, D.; Navaza, J.M.; Quintans-Riveiro, L.C. Viscosimetric behavior of carboxymethyl cellulose-Arabic gum mixtures: A new step to modeling,Carbohydrates Polymers 2010, 80, 26-30.
Hayta M.; Schofield J.D. Dynamic- Rheological behavior of wheat glutens during heating. Journal of the Science of Food and Agriculture 2005, 85, 1992-1998.
Kayacier, A.; Dogan, M. Rheological properties of some gums-salep mixed solutions. Journal of Food Engineering 2006, 72, 261-265.
Mao, Ching-Feng; Rwei, Syang-Peng.Cascade analysis of mixed gels of xanthan and locust bean gum. Polymer 2006, 47, 7980-7987.
Mehdizadeh, B.; Movagharnejad, K. A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide, Fluid Phase Equilibr. 2011, 303, 40-44.
Mingzhi, H.; Ma, Y.; Jinquan, W.;Yan,W. Simulation of a Paper Mill Wastewater Treatment Using a Fuzzy neural Network. Expert. Syst. Appl. 2009, 36, 3, 5064-5070.
Norziah, M.H.; Foo, S.L.; Karim, AAbd. Rheological studies on mixtures of agar (Gracilaria changii) and ?–carrageenan. Food Hydrocolloids 2006, 20, 204-217.
Pai, V.; Srinivasarao, M.; Khan, S.A. Evolution of microstructure and rheology in mixed polysaccharide systems. Macromolecules 2002, 35, 1699-1707.
Pai, V.B.; Khan, S.A. Gelation and rheology of xanthan/enzyme-modified guar blends. Carbohydrates Polymers 2002, 49, 207-216.
Prado, B.M.; Kim, S.; Özen, B.F.;Mauer, L.J. Differentiation of carbohyrate gums and mixtures using fourier transform infrared spectroscopy and chemometrics. Journal of Agricultural and Food Chemistry 2005, 53, 2823-2829.
Ross, T. Predictive food microbiology models in the meat industry. Meat and livestock Australia, Sydney, Australia, ISBN 0958582513,1999,196 pp.
Sozer, N. Rheological properties of rice pasta dough supplemented with proteins and gums. Food Hydrocolloids 2009, 23, 849-855.
Steffe, J.F. Rhelogical methods in food process engineering (2nd ed.) East Lansing: Freeman Press, 1996.
Tako, M.; Nakamura, S. Synergistic interaction between kappa carrageenan and locust bean gum in aqueous media. Agric. Biol. Chem. 1986, 50, 2817-2822.
Tipvarakarnkoon, T.; Senge B. Rheological behaviour of gum solutions and their interactions after mixing. Annual Transaction of the Nordic Rheology Society 2008,Vol.16.
Tokatli, F.;Tari, C.; Unluturk, M.; Baysal, N.G. Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235. Journal of Indian Microbilogy and Biotechnology 2009, 36, 1139-1148.
Toker, O.S.; Dogan, M.; Can?y?lmaz, E.; Ersoz, N.B.; Kaya, Y. The effects of different gums and their interactions on the rheological properties of a pudding: a mixture design approach. Food Bioprocess Technology 2013,6, 896-908.
Toker, O.S.; Dogan, M. Effect of temperature and starch concentration on the creep/recovery behaviour of the grape molasses:modelling with ANN, ANFIS and response surface methodology. European Food Research and Technology 2013,236, 1049-1061.
Y?lmaz, M.T.Comparison of effectiveness of adaptive neuro-fuzzy ?nference system and artificial neural networks for estimation of linear creep and recovery properties of model meat emulsions. Journal of Texture Studies 2012,43, 384-399.
Yilmaz, M.T.; Karaman, S.; Dogan, M.;Yetim, H.; Kayacier, A. Characterization of O/W model system meat emulsions using shear creep and creep recovery tests based on mechanical simulation models and their correlation with texture profile anaylsis (TPA) parameters. Journal of Food Engineering 2012 108(2), 327-336.
Zhang, C.; Quek, S.Y.; Lam, G.; Easteal, A.The rheological behavior of low fat soy-based salad dressing. International Journal of Food Science and Technology 2008,43, 2204-2212.
Bas ? D.; Boyaci, I.H. Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. J. Food. Eng. 2007,78, 846-854.
Bayarri, S.; Dolz, M.; Hernandez, M.J. Effect of carboxymethyl cellulose concentration on rheological behavior of milk and aqueous systems. A creep and recovery study. Journal of Applied Polymer Science 2009,114,1626-1632.
Box, G.; Behnken D. Some new three level designs for the study of quantitative variables. Technometrics 1960, 2, 455-475.
Copetti, G.; Grassi, M.; Lapasin, R.; Pricl, S. Synergistic gelation of xanthan gum with locust bean gum: a rheological investigation, Glucoconjugate J.1997,14, 951-961.
Dogan, M.; Ersoz, N.B.; Toker, O.S.; Kaya, Y.; Can?y?lmaz, E. Optimization of gum combination for instant pudding based on creep and recovery parameters by mixture design approach, European Food Research and technology 2014, 238, 47-58.
Dolz, M.; Hernandez, M.J.; Delegido, J. Creep and recovery experimental investigation of low oil content food emulsions, Food Hydrolcolloid 2008, 22, 421-427.
Garci-Abuin, A.; Gomez-Diaz, D.; Navaza, J.M.; Quintans-Riveiro, L.C. Viscosimetric behavior of carboxymethyl cellulose-Arabic gum mixtures: A new step to modeling,Carbohydrates Polymers 2010, 80, 26-30.
Hayta M.; Schofield J.D. Dynamic- Rheological behavior of wheat glutens during heating. Journal of the Science of Food and Agriculture 2005, 85, 1992-1998.
Kayacier, A.; Dogan, M. Rheological properties of some gums-salep mixed solutions. Journal of Food Engineering 2006, 72, 261-265.
Mao, Ching-Feng; Rwei, Syang-Peng.Cascade analysis of mixed gels of xanthan and locust bean gum. Polymer 2006, 47, 7980-7987.
Mehdizadeh, B.; Movagharnejad, K. A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide, Fluid Phase Equilibr. 2011, 303, 40-44.
Mingzhi, H.; Ma, Y.; Jinquan, W.;Yan,W. Simulation of a Paper Mill Wastewater Treatment Using a Fuzzy neural Network. Expert. Syst. Appl. 2009, 36, 3, 5064-5070.
Norziah, M.H.; Foo, S.L.; Karim, AAbd. Rheological studies on mixtures of agar (Gracilaria changii) and ?–carrageenan. Food Hydrocolloids 2006, 20, 204-217.
Pai, V.; Srinivasarao, M.; Khan, S.A. Evolution of microstructure and rheology in mixed polysaccharide systems. Macromolecules 2002, 35, 1699-1707.
Pai, V.B.; Khan, S.A. Gelation and rheology of xanthan/enzyme-modified guar blends. Carbohydrates Polymers 2002, 49, 207-216.
Prado, B.M.; Kim, S.; Özen, B.F.;Mauer, L.J. Differentiation of carbohyrate gums and mixtures using fourier transform infrared spectroscopy and chemometrics. Journal of Agricultural and Food Chemistry 2005, 53, 2823-2829.
Ross, T. Predictive food microbiology models in the meat industry. Meat and livestock Australia, Sydney, Australia, ISBN 0958582513,1999,196 pp.
Sozer, N. Rheological properties of rice pasta dough supplemented with proteins and gums. Food Hydrocolloids 2009, 23, 849-855.
Steffe, J.F. Rhelogical methods in food process engineering (2nd ed.) East Lansing: Freeman Press, 1996.
Tako, M.; Nakamura, S. Synergistic interaction between kappa carrageenan and locust bean gum in aqueous media. Agric. Biol. Chem. 1986, 50, 2817-2822.
Tipvarakarnkoon, T.; Senge B. Rheological behaviour of gum solutions and their interactions after mixing. Annual Transaction of the Nordic Rheology Society 2008,Vol.16.
Tokatli, F.;Tari, C.; Unluturk, M.; Baysal, N.G. Modeling of polygalacturonase enzyme activity and biomass production by Aspergillus sojae ATCC 20235. Journal of Indian Microbilogy and Biotechnology 2009, 36, 1139-1148.
Toker, O.S.; Dogan, M.; Can?y?lmaz, E.; Ersoz, N.B.; Kaya, Y. The effects of different gums and their interactions on the rheological properties of a pudding: a mixture design approach. Food Bioprocess Technology 2013,6, 896-908.
Toker, O.S.; Dogan, M. Effect of temperature and starch concentration on the creep/recovery behaviour of the grape molasses:modelling with ANN, ANFIS and response surface methodology. European Food Research and Technology 2013,236, 1049-1061.
Y?lmaz, M.T.Comparison of effectiveness of adaptive neuro-fuzzy ?nference system and artificial neural networks for estimation of linear creep and recovery properties of model meat emulsions. Journal of Texture Studies 2012,43, 384-399.
Yilmaz, M.T.; Karaman, S.; Dogan, M.;Yetim, H.; Kayacier, A. Characterization of O/W model system meat emulsions using shear creep and creep recovery tests based on mechanical simulation models and their correlation with texture profile anaylsis (TPA) parameters. Journal of Food Engineering 2012 108(2), 327-336.
Zhang, C.; Quek, S.Y.; Lam, G.; Easteal, A.The rheological behavior of low fat soy-based salad dressing. International Journal of Food Science and Technology 2008,43, 2204-2212.
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Jan 21, 2016
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