Recent advances in thermophysical properties—measurements, prediction, and importance

Abstract

Measurement and prediction of density, specific heat, and thermal conductivity are critical to heat transfer calculations in foods. In this review, we outline the significant changes in the area of predictive modeling of thermophysical properties over the last two decades. There has been a rise in the number of researchers applying sophisticated artificial neural networks, fuzzy logic, and so on to study the non-linear dependency of thermophysical properties on temperature, pressure, and formulation. The refinement and validation of predictive models rely on accurate measurements of thermophysical properties. New developments in instrumentation such as three-dimensional imaging and modifications to calorimetric or line heat source methods have encouraged researchers to employ cutting-edge technology for density, specific heat, and thermal conductivity measurements. Though advancements have been made in adopting new scientific principles, finding the best approach for measuring each property using superior instrumentation and advanced data analysis software is a work in progress.