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Predictive models for goat cheese yield using milk composition

S. S. Zeng, K. Soryal, B. Fekadu, and M. Villiquiran

E (Kika) de la Garza American Institute for Goat Research, Langston University, Langston, OK

Prediction of yield and quality of different types of cheeses that could be produced from a given type and/or amount of goat milk is of great economic benefit to goat milk and cheese producers. Bulk tank goat milk was used for manufacturing hard, semi-hard and soft cheeses (N = 25, 25 and 24, respectively) during a whole lactation to develop predictive models of cheese yield based on milk composition. Percentages of fat, total solids, total protein and casein in milk and moisture-adjusted cheese yield were determined to establish relationships between milk composition and cheese yield. In soft cheese, individual components of goat milk or a combination of two or three components predicted cheese yield with a reasonably high correlation coefficient (r2 = 0.73-0.81). However, correlation coefficients of predictions were lower for both semi-hard and hard cheeses. Overall, total solids of goat milk was the best indicator of yield in all three types of cheese, followed by fat, total protein and casein. When compared with moisture-adjusted cheese yield, the developed yield formulae in this study predicted yield of semi-hard and hard cheeses as well as the standard formula used for cow cheese (Van Slyke formula). In soft cheese, however, the Van Slyke formula under-estimated, while the newly derived formulae over-estimated yield. Further validation of the yield predictive models for hard and semi-hard cheeses of goat milk using larger data sets over several lactations might be needed, because of variations in relationships between milk components due to breed, stage of lactation, season and feeding regime.


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