Thermal Food Processing

18 Thermal Food Processing: New Technologies and Quality Issues
are nonlinear functions of the moisture content, due to significant changes of bulk
porosity; at moistures higher than 30%, k increases linearly with the moisture
content. 4 Despite the attempts in developing structural models to predict the thermal
conductivity of foods, a generic model does not exist at the moment. 36
Since theoretical models have a number of limitations for application in food
material, empirical models are popular and widely used for food process design
and control, even though they are valid only for a specific product and experimental
conditions. 59
Similar to specific heat, most of the models used to calculate the thermal
conductivity of foods with high moisture content have the following form: 27
(1.34)
where c 1 and c 2 are constants. At x w = 1, most of the equations converge on the
thermal conductivity of water. Predictions agree at high water contents, and
discrepancies between experimental and predicted values are marked at low water
contents. Table 1.3 lists some simple equations, which only take into account the
water content of the food (x w is in decimal form). Linear models similar to
TABLE 1.3
Simple Thermal Conductivity Equations for Foods
Material Model Reference
Fruits and vegetables
Tomato paste
Tomato paste
Tomato paste
Spring wheat (hard red)
Dairy products and margarine
Meats and fish
Fish
Minced meat
Fruit juice
Sorghum
Pistachio
k = c1 + c2 xw, k = 0. 148 + 0. 493xw
0 < xw < 0.60
k = 0. 029 + 0. 793xw
0.538 < xw< 0.708, T = 30°C
k =− 0. 066 + 0. 978xw
0.538