Milk protein concentrates (MPCs) are complete dairy proteins (containing both caseins and whey proteins) that are available in protein concentrations ranging from 42% to 85%. As the protein content of MPCs increases, the lactose levels decrease. MPCs are produced by ultrafiltration or by blending different dairy ingredients. Although ultrafiltration is the preferred method for producing MPCs, they also can be produced by precipitating the proteins out of milk or by dry-blending the milk proteins with other milk components. MPCs are used for their nutritional and functional properties. For example, MPC is high in protein content and averages approximately 365 kcal/100 g. Higher-protein MPCs provide protein enhancement and a clean dairy flavor without adding significant amounts of lactose to food and beverage formulations. MPCs also contribute valuable minerals, such as calcium, magnesium, and phosphorus, to formulations, which may reduce the need for additional sources of these minerals. MPCs are multifunctional ingredients and provide benefits, such as water binding, gelling, foaming, emulsification, and heat stability. This article will review the development of MPCs and milk protein isolates including their composition, production, development, functional benefits, and ongoing research. The nutritional and functional attributes of MPCs are discussed in some detail in relation to their application as ingredients in major food categories.

Control of a product's market acceptability can be a dificulty when using linear programming models in food formulation. The development of an acceptability constraint was demonstrated for a linear programming model used for the formulation of fresh turkey bratwurst, a coarse ground type sausage. Development was in two stages. First, an experimental design and in-house panel determined quantitative relationships between the product's textural attributes and turkey meat ingredients. Second, the product toughness relationship was utilized to develop three formulations with diflerent levels of toughness. These formulations were market tested using the acceptor set size as the measure of market acceptability. A relationship between product toughness and acceptor set size was determined, into which was substituted the toughness f (ingredients) relationship. This yielded acceptor set size as a f (ingredients) that was added to the least cost linear programming model in the form of an acceptability constraint.