The effects of temperature and muscle composition on the thermal conductivity of frozen meats (original) (raw)
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Journal of Food Processing and Preservation, 2010
ABSTRACTThermal conductivity values of meat samples with moisture contents between 4.73 and 79.47% (wet basis) and fat contents between 1.44 and 93.17% (wet basis) were measured at temperatures ranging from−30 to 25C using the line heat source probe method. Thermal conductivities of frozen meat samples were higher than the ones in the unfrozen state. Measured thermal conductivity values were mathematically interpreted as a function of temperature, moisture, protein and fat contents by application of nonlinear regression analysis for frozen samples. Measured thermal conductivities were compared with the models given in the literature. Levy's model provided more accurate predictions than the others in the frozen state and parallel model showed the best predictions in the unfrozen state. For unfrozen state, thermal conductivity was found to increase with moisture content and decrease with fat content, although in the frozen state, thermal conductivity increases with decreasing temperature.Thermal conductivity values of meat samples with moisture contents between 4.73 and 79.47% (wet basis) and fat contents between 1.44 and 93.17% (wet basis) were measured at temperatures ranging from−30 to 25C using the line heat source probe method. Thermal conductivities of frozen meat samples were higher than the ones in the unfrozen state. Measured thermal conductivity values were mathematically interpreted as a function of temperature, moisture, protein and fat contents by application of nonlinear regression analysis for frozen samples. Measured thermal conductivities were compared with the models given in the literature. Levy's model provided more accurate predictions than the others in the frozen state and parallel model showed the best predictions in the unfrozen state. For unfrozen state, thermal conductivity was found to increase with moisture content and decrease with fat content, although in the frozen state, thermal conductivity increases with decreasing temperature.PRACTICAL APPLICATIONSThermal properties of food products are key factors in the design of thermal processes such as cooling or heating for food preservation. Analysis, design and simulation of food freezing and storage process demands reliable and easily accessible thermal property data across a wide range of temperatures, particularly below freezing. Thermal conductivity is an important property for freezing and thawing applications. An accurate knowledge of thermal conductivity as a function of composition and temperature is important to determine process parameters involved in heat transfer. Then, the total amount of heat to be added or removed from a product in a specific process can be determined as well as the rate at which heat can be added or removed. The model developed in this study can be used to model heat transfer calculations in freezing, thawing and storage of meats.Thermal properties of food products are key factors in the design of thermal processes such as cooling or heating for food preservation. Analysis, design and simulation of food freezing and storage process demands reliable and easily accessible thermal property data across a wide range of temperatures, particularly below freezing. Thermal conductivity is an important property for freezing and thawing applications. An accurate knowledge of thermal conductivity as a function of composition and temperature is important to determine process parameters involved in heat transfer. Then, the total amount of heat to be added or removed from a product in a specific process can be determined as well as the rate at which heat can be added or removed. The model developed in this study can be used to model heat transfer calculations in freezing, thawing and storage of meats.
A model for the thermal conductivity of frozen meat
Meat Science, 1977
Even though extensive work on the experimental determination of" the thermal conductivities o./'JbodstuJ]s at different temperatures has been published, only a Jew predictive models for this important property have been developed. Calculation o f freezing times inJbods, such as meat, over the range_from-I °C to-30 °C, requires the use of mathematical models in which information on the thermal conductivity of partially f~'o-en meat as a function of ice content in the tissue is provided. In the present paper a model fbr the thermal conductivity oJmeat as a fimction of" temperature, which also accounts for its anisotropic properties, is proposed. Both directions, parallel and perpendicular to meat fibres, are considered and the model applies to unfrozen as well as to partially J~'o:en meat. Res,dts show good agreement with published experimental data obtained by a stead)' state method for different temperatures. NOM ENCLATURE A,B,C et, e2, fl, 1"2 F ka kc kcl. k~p Constants defined in eqns. (15), (16) and (17) respectively. Coefficients in eqns. (19) and (20). Constant in eqn. (18). Water thermal conductivity (W/mK). Thermal conductivity of the continuous matrix. Thermal conductivity of the continuous matrix parallel to the meat fibre direction. Thermal conductivity of the continuous matrix perpendicular to the meat fibre direction. 235 Meat Science (i) (1977)-~ Applied Science Publishers Ltd. England. 1977
Journal of Food Engineering, 1999
Good knowledge of thermophysical characteristics of a wide range of foodstus has a major importance for the accurate prediction of their unsteady-state temperature distribution, the process duration and energy consumption in cooling and freezing (heating and thawing). Such information is necessary to predict the microbiology and biochemistry of spoilage and to control the product safety and quality, as well as for the design, optimisation and ecient operation of refrigerating and thermal systems in the food and biotechnological industries. The purpose of this paper is to present reliable uni®ed equations for determination of the speci®c heat capacity, enthalpy, thermal conductivity, Kirchho function, etc. on the basis of generalised parameters (moisture content, actual and initial freezing temperatures). The relationship between the volumetric speci®c enthalpy and the Kirchho function is also derived. The proposed formulae have large areas of application. They cover practically all industrially processed food materials except those consisting mainly of fats. The equations may easily be used for both simple and rapid engineering calculations and for implementation in more sophisticated mathematical models and computer software, including the cases in which advanced enthalpy methods for numerical heat transfer simulations are involved. Ó
Thermal Properties of Frozen Food: A Review Article
International Journal of Engineering Applied Sciences and Technology, 2020
Food is a mixture of solutes and water particles. The properties of the food material vary by the state of water particles present in them. The density, viscosity are some of the factors that attribute to the difference in thermal properties between frozen and unfrozen food. Several studies have been made in analyzing the thermal properties of frozen food of different varieties from frozen soup pouches to frozen meat. Several properties related to thermic properties such as thermal conductivity, latent heat, heat capacity is also under consideration. For instance, density is found using the known volume of the sample, porosity using water, and so on. Thus thermal properties of frozen food are of immense importance for developmental processes.
The prediction of freezing meat inside the cold storage is studied experimentally and numerically using CFX14.5. In the present work a prototype cold storage for meat has been designed and constructed with dimensions 1 m in length x 1 m in width x 1 m in height. Temperature distributions of regular shape of meats were determined for storage temperature-21°C inside the cold storage, where each part of meat is located in one of the three levels (bottom, medium and top) inside the cold store. The air velocity distribution has been measured by using metal vane anemometer in the directions of (x,-x, y,-y, z and-z) around the meat and the results have been used in the numerical simulations. In the numerical simulations the temperature distributions are based on transient, Navier-Stokes equations, turbulence is taken into account using a standard model for air flow and assumed as steady turbulent state, meats are presented as solid domain with variable thermophysical properties as function of temperature and mass and heat transfer due to evaporation are regulated due to including product casings. During the freezing the properties of meat change during the three stages each stage having specific properties. The minimum temperature of the product was located in the top level and very close to their surrounding storage air temperature both due to exposure to higher air velocity from the fans. The total error of compression between the experimental and numerical temperature distributions of meats is equal to 18.7%.
Estimation of time variable heat transfer coefficients in frozen foods during storage
Journal of Food Engineering, 1992
Many investigators have studied heat transfer coefficients in foods during freezing, but little work has been done on the estimation of surface heat transfer coeficients in frozen foods during storage. This paper investigates a method for the estimation of time variable surface heat transfer coefficients in frozen foods during storage. This method is unique in that it allows for the estimation of surface heat transfer coefficients as a function of time and in that the procedure incorporates temperature dependent thermal properties. The estimation method is based on the sequentialregularization solution which involves the minimization of a least squares function containing calculated and experimental temperatures. Certain parameters inherent in this method were first determined using simulated data. Experimental temperature measurements were then obtained using the Karlsruhe Test Substance (Gutschmidt, I%O), a methylcellulose material commonly used in food freezing studies. Estimations of the surface heat transfer coeficient from this data varied with changes in the surface and ambient conditions; the estimated values ranged from 6 to 12 W/m' "C.
A Simplified Analytical Model for Thawing Time Calculation in Foods
Journal of Food Science, 1989
A simplified analytical model for the thawing time prediction of simpie-shaped foodstuffs was developed. It was assumed in the model that the solution to the unsteady state, unidirectional heat conduction equation with constant thermophysical properties was valid throughout the thawing operation. The latent heat effects were incorporated into effective diffusivity terms defined for the various phases of the thawing operation. The predictions of the model were compared to experimental and numerical data obtained on thawing of infinite slabs, infinite cylinders, and spheres. The level of agreement between the predictions of the present model, and the experimental and numerical data was quite satisfactory. alytical model for the prediction of freezing times of foods undergoing unidirectional heat conduction. The aim of this work was to extend this model to cover the prediction of thawing times of foods undergoing unidirectional heat conduction. THE STANDARD SERIES solution for unsteady state onedimensional heat transfer to solids having constant physical properties, with a uniform initial temperature Ti after exposure to a constant ambient temperature T, has the general form:
Journal of Food Science, 1983
Since there was no procedure available for estimating freezing time of rectangular or finitely cylindrical food through manual calculations, the present study was initiated to develop such a procedure. For this development, our computerized model was used together with statisti& techniques. A design for screening tests was used to identify eight dimensionless groups, which influences significantly the freezing rate, out of 22 independent groups. Among the eight groups, only two were related to the thermophysical properties of food. Through the joint application of a central composite design of experiments and of the computerized model, algebraic formulae containing the eight significant groups were derived for the freezing time estimation. Their validity was verified through experimental freezing processes.