Modeling counter-current osmotic dehydration process of pork meat in molasses

Abstract

© 2014 Wiley Periodicals, Inc. Osmotic dehydration is recognized as a pretreatment step to meat drying processes. Sugar beet molasses is an excellent medium for osmotic dehydration, primarily because of high dry matter and specific nutrient content. The main aim of here presented study was the development of acceptable empirical mathematical models for the representation of counter-current osmotic dehydration process of pork meat in molasses. The effectiveness of the counter-current process was compared with the co-current process, according to experimental results and the developed mathematical models. Response Surface Methodology and Analysis of Variance were selected to estimate the main effects of observed process variables on mass transfer variables. Developed mathematical models showed good fitting capabilities with experimental data. Maximum obtained counter-current osmotic dehydration process responses were: dry matter content of 72.98%; water loss of 0.57g/g i.s.; solid gain equal to 0.18g/g i.s. and water activity of 0.84, indicating that counter-current process had 32.20% higher total process efficiency compared with co-current process. Practical Applications The osmotic dehydration of food is a commonly used technique for food processing, mostly utilized prior to drying and freezing operations, which reduces energy requirements of these processes. This study investigates mathematical models of the counter-current osmotic dehydration process of pork meat in molasses at different temperatures and durations of the process and also determines the effectiveness of the process in comparison with the co-current process. Developed mathematical models can be considered as precise for process parameters prediction and optimization in experimental and industrial applications. The wide range of processing variables were considered in the model formulation and its easy implementation in a spread sheet using a set of equations makes it very useful and practical for outputs prediction.