File Name: thermal properties of food and agricultural materials .zip
Thermal properties are necessary for the design and control of processes and storage facilities of food materials. This study proposes the measurement of thermal properties using easily constructed probes with specific heat capacity calculated, as opposed to the use of Differential Scanning Calorimeter DSC or other.
- THERMAL PROPERTIES OF FOODS
- Thermal Properties of Foods
- THERMAL PROPERTIES OF FOODS
- THERMAL PROPERTIES OF FOODS
THERMAL PROPERTIES OF FOODS
Thermal properties are necessary for the design and control of processes and storage facilities of food materials. This study proposes the measurement of thermal properties using easily constructed probes with specific heat capacity calculated, as opposed to the use of Differential Scanning Calorimeter DSC or other. Results showed thermal properties were within the range of 0.
These results agree with reports for similar products studied using DSC and commercially available line heat source probes. Empirical models were developed for each property through linear multiple regressions. The data generated would be useful in modeling and control of its processing and equipment design. Thermal properties are those physical properties of a material that are significant in heat transfer problems[ 1 ]. Some of these properties include: dimensional characteristics, density, fluid viscosity, unit surface conductance latent heat, specific heat, thermal conductivity, thermal emissivity, thermal diffusivity, coefficient of thermal expansion etc.
Process thermal controls can only be put to use by the precise knowledge of the thermal properties of the food. The primary thermal properties are specific heat capacity at constant pressure , thermal conductivity and thermal diffusivity. To calculate heat transfer in a food, its thermal properties, geometry and thermal processing condition are used as the major parameters [ 3 ].
Compositional model is used when there are no empirical data for the agricultural product. It calculates the desired parameter based on the composition of product as proposed by an early researcher. According to [ 4 ], it is not always accurate to use the general compositional model to predict thermal properties of each specific food material based on its compositions and temperature because this assumes that each component has the same thermal properties regardless of structure in different food materials.
Differential scanning calorimeter and KD2 pro thermal properties analyser produced by Decagon devices are the equipments that are mostly used in the studies of thermal properties of material but due to their high cost, they are not always available. In view of this, [ 5 ] had proposed a new device to measure the thermal property of polystyrene and compared his results with that produced by the differential scanning calorimeter.
The equipment according to the author is easy to construct. However the thermal properties of high moisture biomaterials may not be easy to be measured using the equipment proposed by[ 5 ]. The equation connecting the primary thermal properties is:.
White radish is an important vegetable in many countries. White radish belongs to the Cruciferae or mustard family and is a biennial. This root is getting so important. Apart from its culinary uses [ 6 ], has it that radish root contains some coumarins, enzymes, organic acids, phenolic compounds and some sulphuric compounds.
To the knowledge of the authors of this work, there is no data for the thermal properties of white radish in the literature. The constructed probes were able to generate data that could be compared to that of similar crops in the literature. The data and the models obtained from this study will be useful for the storage, process design and control of this economically important vegetable. They were further grated using a hand held grater and filled into the sample holder for further analysis.
A line heat source probe was used to measure thermal conductivity. It was designed for this study following the description of [ 2 ].
The probe was made of a stainless steel needle containing a T -type thermocouple and a constantan heating wire as shown in Fig 1A , the stainless steel needle was mm long and 2. The heating wire 0. The heating wire was bent into two equal parts length wise with its ends joint to connecting wires thus having its entire length within the probe. It was calibrated using 0. The cylindrical sample container Fig 1B was a stainless steel tube of mm long, 28 mm diameter and 1. Both ends of the tube were covered with Teflon material- one end was permanently sealed while the other end was temporary sealed after filling in the sample.
The thermal diffusivity probe designed for this study was made of a stainless steel needle containing a T-type thermocouple as shown in Fig 1C. The needle was 0. The length of the thermocouple within the needle was as 69 mm and connected to a data logger.
The prepared material was tightly filled into cylindrical sample container and then covered. The thermal diffusivity probe was then inserted length wise at the center of the tube through the Teflon cover and then the hole through which the needle passed was sealed with a water resistant sealant. The temperature time graph of both the core of the tube sample and the water bath were logged by the data logger until the sample temperature equated the set water bath temperature.
Schematic diagram of the setup was as shown in Fig 1E. Thermal diffusivity was determined using the 1D Fourier equation applied to a cylinder. It has a high precision and does not depend on the exact place which the temperature is measured but has high accuracy when measured at the core of the cylinder [ 7 ]. And the thermal diffusivity was calculated from the curve according to [ 7 ] using Eq 3. Each experiment was done four times and the mean value was reported.
Its prediction was compared to the prediction of the empirical equation of this study and the experimental data. This equation was appropriate because it includes the addition of temperature to moisture content for the prediction of thermal diffusivity.
The experiment was done by inserting the thermal conductivity probe into the center of the cylindrical sample container which was tightly filled with prepared sample. C power supply was preset to 1. The setup was as shown in Fig 1C. When the sample temperature reached equilibrium with the water temperature, the heater wire was energized using the regulated DC power supply consisting of 1.
The resulting temperature rise was measured using the thermocouple located in the probe T type thermocouple and recorded every second over the course of a 5 min period using the data logger [ 2 , 8 ]. A graph of T temperature versus ln t time was plotted and the thermal conductivity was calculated using Eq 5. Each experiment had at least 5 replicates. The prediction equation used for the thermal conductivity was the one proposed by [ 9 ] for the thermal conductivity, its prediction was compared to that predicted by the experimental data and the empirical equation of this study.
The specific heat capacity was calculated using Eq 1. The prediction equation used for the thermal conductivity was the one proposed by [ 9 ] for the specific heat. Its prediction was compared to the experimental data and that predicted by the empirical equation of this study.
The experiments were conducted at four levels of moisture content 6. Each experiment was done at least four times and the average value was used for analysis. The results of the thermal conductivity probe calibration were 0. These were compared to the values published by [ 10 ] which were 0. Fig 2 shows the thermal conductivity of white radish and its variation with both moisture and temperature. For each level of moisture studied, the thermal conductivity of white radish increased with increase in temperature, at a given temperature the thermal conductivity also increased with increase in moisture content.
The effect of moisture on thermal conductivity of radish was higher than that of temperature and more significant. However, at subsequent moisture content of 64, 31 and 6. Higher moisture content exhibited higher thermal conductivity possibly because of the higher the number of ions and dipoles. High temperatures therefore caused high gyration of available ions and dipoles enabling faster heat transfer [ 11 ].
At lower moisture there are fewer ions and dipoles leading to slower heat transfer. This may explain the high effects of moisture content over temperature. The relationship between the temperature and moisture content of radish on the thermal conductivity was done using a multiple regression correlation Eq 6. Table 1 shows the empirical equation for this relationship and its statistical information where T and M respectively represent temperature and moisture content within the range of this study.
The high R 2 and a low standard error of estimate are as a result of a good agreement between the model and measured data. The [ 9 ] model for thermal conductivity Eq 7 was used to estimate and to compare its estimations to that of the empirical model and the experimental data from this study Fig 3 where the empirical equation for thermal conductivity of white radish was Eq 6 and compared to that of [ 9 ]. It was observed that both models followed the same trend.
Bulk thermal diffusivity is mostly calculated in reported literature from thermal conductivity, density and specific heat capacity. The equipment used in measuring the specific heat capacity is usually expensive such as the differential scanning calorimeter , hence a probe was designed to measure the thermal diffusivity in this study.
The value in the reference article [ 7 ] was 1. The maximum value for the diffusivity of white radish in this study was 1. The results of the thermal diffusivity of white radish roots are shown on Fig 4.
The results showed that increase in temperature made the radish roots at particular moisture content be more diffusive. Thermal diffusivity increased linearly with the increase in temperature and moisture. This is in agreement with reported research on agricultural products. A multiple regression was carried out to correlate the effects of moisture and temperature on the thermal diffusivity of white radish Eq 8.
The empirical model and its statistical information are on Table 2 , where T and M respectively are temperature and moisture content within the range of this study. The high F-value, R 2 and low Standard error of estimate indicate a good fit of this model to the experimental values. This is in agreement with earlier researches by [ 4 , 15 , 16 ]. Other researchers also reported increase of thermal diffusivity with increasing moisture content [ 12 , 17 , 18 ].
Fig 5 compares the Martens model Eq 9 , the empirical model Eq 8 and the experimental data. The R 2 for the two models are very close in terms of values. The bulk density ranged between and The specific heat capacity of white radish in this study was between 4.
The reported values in the literature though measured using the differential scanning Calorimeter DSC , are in agreement with our results. The specific heat capacity of radish like the thermal diffusivity and the thermal conductivity increased with the increase in temperature for each level of moisture content studied Fig 6. The correlation between the moisture content and temperature shows that the two factors were highly significant with moisture content being more significant than temperature.
The empirical model, Eq 11 is shown on Table 3 with its statistical information where T and M respectively are temperature and moisture content within the range of this study. The high R 2 , high F- value and low standard error of the estimate indicate the goodness of fit. The model used for the estimation of the specific heat capacity of white radish was the[ 9 ] model for specific heat capacity Eq
Thermal Properties of Foods
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THERMAL PROPERTIES OF FOODS
Thermal properties of food systems at high pressure HP are important in the design and operation of HP processing equipment. Available techniques for thermal property evaluation under HP conditions are still very limited. In this study, a dual-needle line-heat-source DNL device was installed in an HP vessel to evaluate thermal conductivity k , diffusivity alpha , and volumetric heat capacity C pV of foods at high pressure.
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THERMAL PROPERTIES OF FOODS
This textbook on the thermal properties of food and agricultural material has chapters which deal with the following subjects; basic concepts of heat transfer, as related to thermal processing and techniques for determination of available data on specific heat of raw and processed food and feed products, soil and wood a number of illustrated examples are included to demonstrate the use of a given technique or principle ; methods for determinaion of data on thermal conductivity, thermal diffusivity, unit surface conductance or the heat transfer coefficient of foods and agricultural materials; and, the applications of thermal properties in relation to cooling, freezing, drying, heat treatment, heat of respiration, and thermal expansion. Similar records in OSTI. GOV collections:. GOV Book: Thermal properties of foods and agricultural materials. Title: Thermal properties of foods and agricultural materials. Full Record Other Related Research.
Physical Properties of Foods pp Cite as. Since many stages in the processing and preservation of foods involve heat transfer, it is important to understand the thermal properties of foods. Thermal properties data are required in engineering and process design. An energy balance for a heating or cooling process cannot be made and the temperature profile within the material cannot be determined without knowing the thermal properties of the material. In this chapter, principles and measurement methods of thermal conductivity, specific heat, enthalpy, and thermal diffusivity are discussed.