A-priori modelling of selected physico-chemical properties of Malbec V. Vinifera specie Alphonse Lavallee variety of black grape juice

Abstract

Estimation of physico-chemical properties is essential for food processing operations ranging from equipment design to plant installation and extending towards packaging, storage and distribution applications. Among the various physico-chemical properties, density, heat capacity and thermal conductivity play an influential role. Herein, the physico-chemical properties such as estimation of density, heat capacity and thermal conductivity for Malbec V. Vinifera specie Alphonse Lavallee variety of black grape juice are estimated as a function of temperature (274 to 339 K) and concentration (13.6–45°Brix). A group contribution method approach coupled with the genetic algorithm (GA) formulation is employed to estimate the parameters and respective non-linear equations for important property parameter calculations are generated. The objective functions use experimental dataset of density, thermal conductivity and heat capacity over the same temperature and concentration range subtracted from the estimated dataset to minimize the objective function values in the genetic algorithm framework. Additionally, cubic equation of state is employed as constitutive relation for such estimation. The results follow a similar trendline and show a close resemblance in comparison of the experimental dataset with high accuracy indicating a potential use of similar methodology towards estimation of properties for other food matrices.Graphical