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Economic Feasibility Evaluation of Microfiltration of Milk Prior to Cheesemaking

Northeast Dairy Foods Research Center Department of Food Science, Cornell University, Ithaca, NY 14853

Corresponding author: D. M. Barbano; e-mail:
dmb37{at}cornell.edu.

A nonlinear programming optimization model was used to evaluate the net revenues and potential profitability 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.

Key Words: cheese • optimization • microfiltration

Abbreviation key: ACONST2 = the proportion of lactose, NPN, and minerals that should be retained during ultrafiltration of the MF permeate, CF = concentration factor, FATCREAM = percent of fat in cream, FATSKIM = percent of fat in skim milk, FATWHOLE = percent of fat in raw milk, FDB = fat on a dry basis, LACRET = amount of lactose in the retentate, LACSKIM = amount of lactose in skim milk, LACWHOLE = percent lactose in raw milk, LPREC2 = percent recovery of lactose from the UF permeate from SPC production in lactose powder, MAXWHOLE = maximum amount of raw milk allowed, MF = microfiltration, MINRET = amount of minerals in the retentate, MINSKIM = amount of minerals in skim milk, NPNRET = amount of NPN in the retentate, NPNSKIM = amount of NPN in skim milk, NPNWHOLE = percent of NPN in raw milk, PROPCAS = percent of casein in total protein, PROTSKIM = percent of total protein in skim milk, SPC = serum protein concentrate, TPREC = percent recovery of whey protein concentrate, TRPROTRET = amount of true protein in the retentate, TRPROTSKIM = amount of true protein in skim milk, WPC = whey protein concentrate

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				J. A. Burke Two Mathematical Programming Models of Cheese Manufacture J Dairy Sci, February 1, 2006; 89(2): 799 - 809. [Abstract] [Full Text] [PDF]