Improved equation for cryoscopic estimation of water activity in cheese

Ice crystallization and structural changes in cheese during freezing and frozen storage: implications for functional properties

Temperature-mediated preservation techniques offer a simple, scalable, effective, and fairly efficient method of long-term storage of food products. In order to ensure the uninterrupted availability of cheese across the globe, a critical understanding of its techno-functional properties as affected by freezing and frozen storage is essential. Detailed studies of temperature-mediated molecular dynamics are available for relatively simpler and homogeneous systems like pure water, proteins, and carbohydrates. However, for heterogeneous systems like cheese, inter-component interactions at sub-zero temperatures have not been extensively covered. Ice crystallization during freezing causes dehydration of caseins and the formation of concentration gradients within the cheese matrix, causing undesirable changes in texture-functional attributes, but findings vary due to experimental conditions. A suitable combination of sample size, freezing rate, aging, and tempering can extend the shelf life of high- and low-moisture Mozzarella cheese. However, limited studies on other cheeses suggest that effects and suitability differ by cheese type, in most cases adversely affecting texture and functional attributes. This review presents an overview of the understanding of the effects of refrigeration, freezing techniques, and frozen storage on structural components of cheese, most prominently Mozzarella cheese, and the corresponding impact on microstructure and functionality. Also included are the mechanism of ice formation and relevant mathematical models for estimation of the thermophysical properties of cheese to assist in designing optimized schemes for their frozen storage. The review also highlights the lack of unanimity in critical understanding concerning the effect of freezing on the long-term storage of Mozzarella cheese with respect to its functionality.

Combining individual-based modeling and food microenvironment descriptions to predict the growth of Listeria monocytogenes on smear soft cheese

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability.