Literature DB >> 27439148

Multiphase porous media modelling: A novel approach to predicting food processing performance.

Md Imran H Khan1,2, M U H Joardder1, Chandan Kumar1, M A Karim1.   

Abstract

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Keywords:  Multiphase model; baking; cooking; drying; food material; frying; heating; review

Mesh:

Year:  2017        PMID: 27439148     DOI: 10.1080/10408398.2016.1197881

Source DB:  PubMed          Journal:  Crit Rev Food Sci Nutr        ISSN: 1040-8398            Impact factor:   11.176


  1 in total

1.  The Combined Diffusion and Adsorption Concept for Prediction of Nanoparticles Transport through Dermal Layers Based on Experiments in Membranes.

Authors:  Mariola M Błaszczyk; Jerzy Sęk; Łukasz Przybysz
Journal:  Int J Mol Sci       Date:  2022-06-08       Impact factor: 6.208

  1 in total

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