Thermophysical Properties Of Foods Research Articles

Simulações numéricas de tempo de congelamento de alimentos utilizando modelos de propriedades termofísicas de alimentos

This work aims to evaluate the performance of a numerical modeling for prediction of food freezing times by comparing numerical with experimental results. A one-dimensional heat diffusion model including temperature dependent thermophysical properties, sudden variations of thermophysical properties during phase change and surface mass transfer was approximated using the finite differences method. The thermophysical properties of foods were modeled as functions of food composition, temperature and phase, using Choi and Okos correlations (Choi & Okos, 1986). Simple geometries such as thin slab, long cylinder and sphere were modeled, and the numerical results were compared with experimental data obtained in a pilot scale freezing tunnel. Numerical simulations were performed for some selected foods, namely sausages, potatoes, hamburger and cheese, in different geometries and sizes. The boundary conditions of the freezing surfaces were of heat and mass convection. The heat transfer coefficients were taken after usual correlations for these geometries. The mass transfer modeling was done using mass convection correlations, considering a known surface wetness. It was found that mass transfer due to moisture evaporation, thermodependency of properties and an accurate estimate for the heat transfer coefficient were crucial elements for the correct prediction of freezing curves. The numerical results were only able to predict the experimental freezing curve by adjusting the theoretical value of the heat transfer coefficient by a factor, varying from 0.7 to 1.3 in most cases, with some outliers up to 2.4. This means that although the heat conduction inside the food itself seemed to generate reasonable food freezing rates, the convection coefficients produced experimentally seemed to vary wildly from the ones predicted in the theoretical relationships. Therefore, one should be very keen of the magnitude of the convection coefficient while performing predictions for a given food freezing application problem. Keywords: Food freezing, Transient heat conduction, Finite difference, Models for food thermal properties

<b>Thermophysical properties of yacon (<i>Smallanthus sonchifolius</i>): experimental determination and effect of moisture content

The knowledge about thermophysical properties of foods is especially important in thermal processing, influencing the design, optimization and cost reduction of the process, as well as the quality and safety of the final product. This article deals with the determination of some thermophysical properties of yacon, namely, specific mass, specific heat, thermal conductivity and thermal diffusivity during the osmo-convective drying. Yacon is a root with approximately 90% w.b. of moisture content, whose high concentration of fructooligosacharydes and antioxidants has gained attention in the food research field. Yacon slices were osmotically dehydrated for 2 hours in a sucralose solution and then dried in a convective tray dryer for 2 hours, varying the osmotic solution’s temperature and stirring rate and temperature of the drying air. All thermophysical properties were determined during the drying process at 30-minute intervals. The thermophysical properties were determined not only experimentally but also calculated by models available in literature based on the product’s centesimal composition. A satisfactory agreement between experimental and predicted results was obtained. Further, empirical models obtained by nonlinear regression were successfully fitted to the experimental data, as a function of moisture content, within a 94% - 3% w.b. range.

Modeling Thermophysical Properties of Food Under High Pressure

A set of well-known generic models to predict the thermophysical properties of food from its composition at atmospheric conditions was adapted to work at any pressure. The suitability of the models was assessed using data from the literature for four different food products, namely tomato paste, potato, pork, and cod. When the composition of the product considered was not known, an alternative was proposed if some thermal data at atmospheric conditions were available. Since knowledge on the initial freezing point and ice content of food are essential for the correct prediction of its thermal properties, models for obtaining these properties under pressure were also included. Our results showed that good predictions under pressure, accurate enough for most engineering calculations can be made, either from composition data or using known thermal data of the food considered at atmospheric conditions. All the equations and coefficients needed to construct the models are given throughout the text, thus readers can compose their own routines. However, these routines can also be downloaded free at http://www.if.csic.es/programas/ifiform.htm as executable programs running in Windows.

Thermophysical Properties of Lemon Juice as Affected by Temperature and Water Content

To design equipment for food processing and estimate process times for refrigerating, freezing, heating, or drying of foods, the thermophysical properties must be known. Since the thermophysical properties of foods are strongly dependent upon chemical composition and temperature, composition and temperature based models provide a means of estimating these properties. In this work, the thermophysical properties of lemon juice were determined at a water mass fraction of (0.381 to 0.900) and a temperature of (273.45 to 353.75) K. Density and thermal conductivity varied from (962.3 to 1282.8) kg·m−3 and (0.344 to 0.624) W·m−2·K−1, respectively. Heat capacity and thermal diffusivity varied from (2446.5 to 4060.1) J·kg−1·K−1 and (0.1160·10−6 to 0.1785·10−6) m2·s−1, respectively. Simple polynomial functions were fitted to the experimental data, and good agreements were obtained. In the tested range, water content showed greater influence on the thermophysical properties.

Modeling Thermal Conductivity, Specific Heat, and Density of Milk: A Neural Network Approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0°C, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

Evaluation of Thermophysical Property Models for Foods

The thermophysical properties of foods are required in order to calculate process times and to design equipment for the storage and preservation of food. There are a multitude of food items available, whose properties are strongly dependent upon chemical composition and temperature. Composition-based thermophysical property models provide a means of estimating properties of foods as functions of temperature. Numerous models have been developed and the designer of food processing equipment is faced with the challenge of selecting appropriate ones from those available. In this paper selected thermophysical property models are quantitatively evaluated by comparison to a comprehensive experimental thermophysical property data set compiled from the literature. For ice fraction prediction, the equation by Chen (1985b) performed best, followed closely by that of Tchigeov (1979). For apparent specific heat capacity, the model of Schwartzberg (1976) performed best, and for specific enthalpy prediction, the Chen (1985a) equation gave the best results, followed closely by that of Miki and Hayakawa (1996). Finally, for thermal conductivity, the model by Levy (1981) performed best.