Composition-Based Prediction of Temperature-Dependent Thermophysical Food Properties: Reevaluating Component Groups and Prediction Models

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• 作者：David Martin Phinney ; John C. Frelka and Dennis Ray Heldman
• 刊名：Journal of Food Science
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：82
• 期：1
• 页码：6-15
• 全文大小：801K
• ISSN：1750-3841

文摘

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.