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The rinse steps are important components of the CIP operation and have direct impact on the amounts of water and energy used for the entire processing operation. The efficiency of rinse water can be improved significantly by the selection of appropriate combinations of operating parameters. For example, higher velocities of rinse water (2.26 m/s) provide significant improvements on rinse effectiveness when compared to current commercial practice (1.52 m/s). The careful selection of rinse water temperature and velocity can result in overall reductions in water and energy used for cleaning operations. The reuse of water for a 2nd or 3rd pass provides additional opportunities for reducing water requirements without influencing effectiveness.

Prediction of temperature-dependent thermophysical properties (thermal conductivity, density, specific heat, and thermal diffusivity) is an important component of process design for food manufacturing. Current models for prediction of thermophysical properties of foods are based on the composition, specifically, fat, carbohydrate, protein, fiber, water, and ash contents, all of which change with temperature. The objectives of this investigation were to reevaluate and improve the prediction expressions for thermophysical properties. Previously published data were analyzed over the temperature range from 10 to 150°C. These data were analyzed to create a series of relationships between the thermophysical properties and temperature for each food component, as well as to identify the dependence of the thermophysical properties on more specific structural properties of the fats, carbohydrates, and proteins. Results from this investigation revealed that the relationships between the thermophysical properties of the major constituents of foods and temperature can be statistically described by linear expressions, in contrast to the current polynomial models. Links between variability in thermophysical properties and structural properties were observed. Relationships for several thermophysical properties based on more specific constituents have been identified. Distinctions between simple sugars (fructose, glucose, and lactose) and complex carbohydrates (starch, pectin, and cellulose) have been proposed. The relationships between the thermophysical properties and proteins revealed a potential correlation with the molecular weight of the protein. The significance of relating variability in constituent thermophysical properties with structural properties--such as molecular mass--could significantly improve composition-based prediction models and, consequently, the effectiveness of process design.

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Food freezing is a preservation process that works by lowering temperature while simultaneously decreasing water activity. It is accepted that although freezing preserves foods, it generally has a negative effect on textural quality. This research investigated the texture response of potatoes (Solanum tuberosum) as a function of time to freeze (defined as the time for the center temperature to reach -20 °C) and thawing process. Potatoes slices (6 mm) were blanched then frozen in an ethanol/carbon dioxide bath, a pilot scale high velocity air freezer (HVAF) and a still air freezer to achieve various times to freeze. Slices were stabilized at -20 °C and thawed by 2 methods; room temperature air and microwave. Afterwards, samples were allowed to come to room temperature prior to texture profile analysis (TPA). Results indicate a maximum texture loss of the potato was reached at a time to freeze of approximately 8 min (corresponding to the HVAF). The texture difference between room temperature and microwave thawing methods was not shown to be significant (P = 0.05). SEM images showed the cellular structure of the potato in a HVAF to be similar to that of the still air freezer, validating that the matrix was maximally damaged in both conditions. This work created a continuous quality loss model for the potato as a function of time to freeze and showed no textural benefit to high velocity over still air freezing.