Economic feasibility evaluation of microfiltration of milk prior to cheesemaking.
A nonlinear programming optimization model was used to evaluate the net revenues and potential profit-ability of microfiltration (MF) prior to cheesemaking in the 3-year period 1998 to 2000, using monthly milk price and composition data. The model identifies the optimal mix of milk resources and determines if MF cheesemaking produces a higher net revenue than conventional cheesemaking that uses NDM and condensed milk for fortification. This study demonstrates the potential of this model to evaluate new technologies in cheese manufacture and improve decision making in the cheese industry. The use of MF produced higher net revenues in 30 out of the 36 mo for both Cheddar and low-moisture, part-skim mozzarella, leading to an appreciable increase in net revenue (vs. conventional cheesemaking) for both cheeses. The benefit from MF in net revenue was greater when the cream price was high. The use of 3X MF yielded the same net revenue as 2X MF. An estimate of manufacturing costs of MF vs. conventional cheesemaking was also made. To this end, the yields of products were calculated by the optimization model, while the production cost of each product was estimated from data of two economic engineering studies and a MF cheesemaking trial. The manufacturing cost of MF Cheddar was slightly higher than the manufacturing cost of conventional Cheddar. However, the benefit in net revenue from the use of MF was estimated to be higher than the difference in manufacturing costs. Moreover, some advantages in the new coproducts of MF Cheddar could outweigh its higher manufacturing cost. The relationships between prices and recoveries of coproducts required to render MF profitable were identified.
Linear Programming in the Allocation of Milk Resources for Cheese Making
Abstract To use linear programming, the cheese making process can be modeled as a resource conversion process. The relationship between input milk resources and output products can be analyzed using mass balance assumptions. The variables used in the linear programming model will represent the pounds of each milk resource chosen to formulate a vat of cheese milk. Additional variables will be used to represent the pounds of cream to be removed from a milk resource that has the potential to have cream removed. Three objectives that could be considered are 1) maximize net returns, 2) maximize cheese yield, or 3) minimize cost. A hypothetical example is used to illustrate how the cheese making process can be modeled. This linear programming model depends on formulas that relate ratio of casein to fat of milk to the fat on the dry basis of the cheese. Sensitivity analysis is performed to determine at what cost a milk resource not in the optimal formulation will replace a milk resource currently in the optimal formulation. The linear programming model can be an effective management tool in the allocation of milk resources for cheese making.
Activation of Plasmin with Urokinase in Ultrafiltered Milk for Cheese Manufacture
Abstract Havarti and Saint-Paulin cheeses were manufactured by traditional and UF techniques. Urokinase and KIO 3 were added individually and together to batches of retentate before cheese making. Cheeses were analyzed for solids, fat, total N, pH 4.6 soluble N, NPN, plasmin activity, plasmin plus plasminogen activity, and casein degradation. Eighty cheese samples were analyzed (two varieties, two replications, and five treatments sampled after 1, 28, 56, and 84 d). Urokinase increased plasmin activity in UF Havarti and Saint-Paulin cheeses. This increased pH 4.6 soluble N levels and NPN, increased proteolysis of β-casein, and slightly improved the flavor profile (only Havarti). Potassium iodate in UF cheese inhibited starter organisms, did not influence plasmin activity, and did not improve ripening. Activation of plasminogen may aid in ripening some UF cheese varieties.