Innovative Uses of Milk Protein Concentrates in Product Development
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".