An Empirical Method for Prediction of Cheese Yield

Abstract

Theoretical cheese yield can be estimated from the milk fat and casein or protein content of milk using classical formulae, such as the VanSlyke formula. These equations are reliable predictors of theoretical or actual yield based on accurately measured milk fat and casein content. Many cheese makers desire to base payment for milk to dairy farmers on the yield of cheese. In small factories, however, accurate measurement of fat and casein content of milk by either chemical methods or infrared milk analysis is too time consuming and expensive. Therefore, an empirical test to predict cheese yield was developed which uses simple equipment (i.e., clinical centrifuge, analytical balance, and forced air oven) to carry out a miniature cheese making, followed by a gravimetric measurement of dry weight yield. A linear regression of calculated theoretical versus dry weight yields for milks of known fat and casein content was calculated. A regression equation of y=1.275x + 1.528, where y is theoretical yield and x is measured dry solids yield (r2=0.981), for Cheddar cheese was developed using milks with a range of theoretical yield from 7 to 11.8%. The standard deviation of the difference (SDD) between theoretical cheese yield and dry solids yield was 0.194 and the coefficient of variation (SDD/mean×100) was 1.95% upon cross validation. For cheeses without a well-established theoretical cheese yield equation, the measured dry weight yields could be directly correlated to the observed yields in the factory; this would more accurately reflect the expected yield performance. Payments for milk based on these measurements would more accurately reflect quality and composition of the milk and the actual average recovery of fat and casein achieved under practical cheese making conditions.