MODELING OF TEMPERATURE DISTRIBUTION IN SHRIMP, AND MEASUREMENT OF ITS EFFECTS ON TEXTURE, SHRINKAGE AND YIELD LOSS
The quality of processed shrimp is partly determined by microbiological and textural attributes, and yield loss is economically important. Temperature is the most important variable that affects the above. A mathematical model was developed for the prediction of temperature distribution during cooking of shrimp. The model used a finite difference approach, and assumed a circular cross-sectional area of shrimp, variable thermal conductivity, specific heat, density, as well as changes in dimensions due to shrinkage.
The number of surviving Listeria monocytogenes (PkF1) inoculated into the slowest heating point of shrimp were experimentally determined, and compared with the predictions of the model. Experimental survivor numbers were much lower than model predictions. This may be due to the D and z values used taken from the literature.
In texture and yield loss experiments, shrimp were cooked in water at 55, 65, 75, 85 and 95 C for two different cooking times (time for the slowest heating point of shrimp to reach the cooking water temperature, and 1.5 times this duration), and the changes in textural parameters, yield losses and moisture content changes were measured. The textural changes were measured by an Instron Universal Testing Machine and sensory methods. The shrinkage in the different segments of shrimp were also determined. Cooling was accomplished in ziploc bags in ice water. Shrimp were also cooked in water at 75, 85, 95 and 100 C for different periods. In the calculation of cooking times by the model, Vibrio cholera was selected as target organism for a 6 log cycle reduction at the coldest point. Shrimp were cooled in ice water and in ziploc bags immersed in ice water. The purpose was to see if water absorption affects the texture, dimensions, and yield loss. Then, the textural changes (Instron Universal Testing Machine), yield losses and moisture changes were determined. In all experiments, tiger shrimp with three sizes -large (35-44/kg), medium (90-110/kg) and small (110-132/kg) - were used.
The mathematical model can accurately predict the temperature distribution in shrimp. Later modifications to the model can also predict the yield loss and shrinkage, texture and reduction in the microbial load of the shrimp. This tool can then be used to optimize the cooking parameters of tiger shrimp.