Effect of temperature on rheological characteristics of molasses: Modeling of apparent viscosity using Adaptive Neuro – Fuzzy Inference System (ANFIS)

Karaman, Safa; Kayacıer, Ahmed

doi:10.1016/j.lwt.2011.03.004

Cited by 33 publications

(23 citation statements)

References 16 publications

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“…Using this test, variation of dynamic mechanical spectra of honey varieties versus increased stress was characterized and LVR of samples over a stress range of 0.1-10 Pa at constant frequency (1 Hz) was determined since LVR is essential for the frequency sweep test application. In an oscillatory frequency sweep test, dynamic mechanical spectra of the honey sample were evaluated in the frequency range of 0.1-10 Hz at constant stress (within the range of LVR) at three different temperatures (10,15, and 20 • C). Low temperatures were selected for the rheological measurements since honey is very sensitive to the temperature.…”

Section: Dynamic Mechanical Analysismentioning

confidence: 99%

“…7f and 8f). Karaman and Kayacier [15] applied the ANFIS modeling techniques for the prediction of the apparent viscosity of date and apricot molasses and they concluded that the ANFIS model can be efficiently used for the prediction of apparent viscosity of molasses with coefficients of determination greater than 0.979. Similarly, Gänzle et al [35] stated that fuzzy inference systems can be efficiently used with the precise rules for the Downloaded by [George Mason University] at 15:28 02 January 2015 construction of effective predictive models to estimate the different parameters in nonlinear systems.…”

Section: Rheological Characteristicsmentioning

confidence: 99%

“…There are different studies related to application of these nonlinear modeling techniques to estimate the different parameters of food samples, and effective predictive models were constructed. [13][14][15][16] In our previous work, some Turkish honey samples were rheologically characterized. [4] All honey samples studied showed Newtonian behavior in steady state shear rheological measurements.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Mechanical Spectra of Selected Turkish Honeys: Construction of Predictive Models for Complex Viscosity Using Two Different Nonlinear Modeling Techniques

Kayacıer

Yüksel

Karaman

2013

International Journal of Food Properties

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Dynamic oscillatory shear rheological characteristics of honey samples from different floral sources were evaluated using frequency sweep test and stress sweep test at three different temperatures (10, 15, and 20 • C). All honey samples showed liquid-like behavior because the loss modulus was significantly greater than the elastic modulus. Pine honey showed the highest complex viscosity (86.33 Pa s at 10 • C), while the lowest complex viscosity was observed in citrus honey (22.15 Pa s) at the same temperature. Temperature dependency of complex viscosity of honey samples was modeled by Arrhenius model and activation energy values of honeys ranged from 83.13 to 97.46 kJ/mol. An efficient predictive model for complex viscosity values of honeys was constructed using adaptive neuro fuzzy inference system that showed satisfactory prediction performance with high coefficient of determination (0.995) and low root mean square error (0.04) in the validation period compared to artificial neural networks.

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Section: Dynamic Mechanical Analysismentioning

confidence: 99%

Section: Rheological Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Mechanical Spectra of Selected Turkish Honeys: Construction of Predictive Models for Complex Viscosity Using Two Different Nonlinear Modeling Techniques

Kayacıer

Yüksel

Karaman

2013

International Journal of Food Properties

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“…Rheological characteristics of foods are dependent on both the initial materials properties (such as initial soluble solids, particle size, and added thickening substances) and processing conditions (such as temperature and levels of shear rate) (Karaman & Kayacier, ; Quek et al, ; Toker & Dogan, ). Among these, concentration and temperature have been shown to be the most important factors affecting the rheological behavior of food product (Cristina dos Santos Bofo et al, ; Deshmukh, Manjunatha, & Raju, ; Quek et al, ).…”

Section: Introductionmentioning

confidence: 99%

Rheological behavior of different phases of grape molasses after storage: Effect of concentration and temperature

Milani

Naghavi

Golkar

et al. 2019

J Food Process Preserv

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The objective of this study was to investigate the effects of concentration (53, 63, and 73°Brix) and temperature (25, 45, and 65°C) on the rheological behavior of grape molasses after 6 months of storage at the ambient condition (25 ± 2°C). According to the results, a pseudoplastic behavior was observed at different temperatures and concentrations for both red and brown phases of the samples. The power law model revealed the best fit to the rheological data and its parameters (K and n) were determined. After storage, the red phase of grape molasses had different K and n values compared to the brown phase. Furthermore, the influences of temperature and concentration on the K were successfully fitted to the studied model. The flow activation energy (Ea) of grape molasses samples was 20.41–58.53 kJ/mol. A quadratic polynomial relationship was also found between the estimated values of Ea and concentrations with high R2. Practical applications This work aimed to study the effect of concentration–temperature combinations on the rheological behavior of different phases of grape molasses after 6 months of storage. The results demonstrated that the storage period affected the rheological behavior grape molasses. In turn, this change affects the quality of the product. Furthermore, rheological behavior of the samples was well predicted by the studied models. These models describing the combined effect of temperature and concentration on the rheological parameters are very valuable for engineering applications.

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“…The adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models are nonlinear models that have been commonly used in the food industry to model input-output relationships (Jang and Sun, 1995), since foods are very complex systems. In the literature, there have been many studies about establishment of ANN or ANFIS models to predict rheological parameters (Ghoush and Samhouri, 2008;Mohebbi et al, 2008;Karaman and Kayacier, 2011;Yalcin et al, 2012b;Yılmaz, 2012;Öztürk et al, 2013;Dogan, 2013, Toker et al, 2013a). Although ANN and ANFIS models are sufficient for the prediction of dependent variables, they do not provide information about the relationship between the dependent and independent variables.…”

Section: Introductionmentioning

confidence: 99%

Modeling of rheological properties of mellorine mix including different oil and gum types by combined design, ANN, and ANFIS models

Karasu¹,

Doğan²,

Toker³

et al. 2014

Turk J Agric For

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IntroductionCost is one of the main factors affecting consumer food product preferences. For that reason, low-cost production of alternative products has been of interest to food researchers. Mellorine, as an alternative to ice cream, is a good example of such a product. Mellorine is an ice cream or frozen dessert product in which all or some proportion of the milk fat is substituted with plant-based oil (Clarke, 2004;Keeney, 2012). Usage of vegetable oil instead of milk fat promotes human health, since milk fat contains saturated fatty acid in concentrations of approximately 60%-70% (Nadeem et al., 2009) and 0.25%-0.38% cholesterol (Mathur et al., 1999). The use of vegetable oil in ice cream formulation results in a balanced saturated and unsaturated fatty acid composition in the product and lower cholesterol content (Nadeem et al., 2009). Mellorine is widely consumed throughout the world due to its lower cost, cholesterol content, and saturated fatty acid composition compared to ice cream. However, the low cost of the product is not the only factor affecting consumer preference. In addition to cost, customers also take into consideration the textural and sensorial properties of the product. Therefore, it is important both for the cost and the product's acceptability to provide the desired textural and sensory properties of mellorine by changing ingredients like oil or hydrocolloid type.The fat or oil type in ice cream has a significant effect on the quality of the end products (Dogan and Akgul, 2005;Rossa et al., 2012), since it affects qualities like the creaminess (Koxholt et al., 2001), texture, and mouthfeel (Adapa et. al., 2000;Dogan and Kayacier, 2007). In addition, fat contributes to the stabilization of the air phase as well as fat aggregation levels through surrounding air bubbles, which improves melting resistance (Granger et. al., 2005) and ice recrystallization (Goff, 2002). Therefore, selecting the optimum oil type is an important factor in improving the quality of mellorine. Substitution of palm olein with milk fat at a level of 3% did not negatively affect the compositional properties, overrun value, flavor, or sensory properties of ice cream (Nadeem et al., 2009). Adhikari and Arora (1994) reported that, regarding textural characteristics, sensory scores of ice cream containing vegetable oils were lower than those of the control sample; this might be avoided by using different ingredients in the

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Effect of temperature on rheological characteristics of molasses: Modeling of apparent viscosity using Adaptive Neuro – Fuzzy Inference System (ANFIS)

Cited by 33 publications

References 16 publications

Dynamic Mechanical Spectra of Selected Turkish Honeys: Construction of Predictive Models for Complex Viscosity Using Two Different Nonlinear Modeling Techniques

Dynamic Mechanical Spectra of Selected Turkish Honeys: Construction of Predictive Models for Complex Viscosity Using Two Different Nonlinear Modeling Techniques

Rheological behavior of different phases of grape molasses after storage: Effect of concentration and temperature

Modeling of rheological properties of mellorine mix including different oil and gum types by combined design, ANN, and ANFIS models

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