Effect of High‐Pressure Processing Operating Parameters on Microbial Inactivation and Bioactive Protein Preservation in Bovine Milk: A Systematic Review
Bibliographic record
Abstract
In the U.S., bovine milk is processed using thermal pasteurization to ensure microbial safety. However, this process alters the structure of heat-sensitive bioactive proteins associated with the functional benefits of raw milk, including antimicrobial, immunomodulatory, and antioxidant proteins. Given the risks associated with raw milk consumption and the negative effects of thermal processing on protein functionality, there is a growing interest in high-pressure processing (HPP), an alternative treatment that may better preserve milk's functional qualities. HPP is widely used in other food sectors but is not yet approved for milk in the U.S. Most studies have investigated either the microbial safety or the preservation of bioactive protein structure in HPP-treated milk, rarely considering both outcomes together. Therefore, optimization of HPP treatments for dairy remains incomplete. The goal of this systematic review was to identify optimal HPP operating parameters for simultaneously achieving microbial inactivation and preserving bioactive proteins in bovine milk. Eighty-nine articles met inclusion criteria from Web of Science, Medline, EMBASE, and PubMed based on a specified search strategy. Pressures ≥600 MPa achieved >5-log average reductions in Listeria monocytogenes, Salmonella enterica, and Staphylococcus aureus, yet often caused considerable denaturation of proteins such as β-lactoglobulin and immunoglobulin G and lesser denaturation of lactoferrin and alkaline phosphatase. Future research on HPP and bovine milk should evaluate both microbial reductions and impacts on nutrients within the same manuscript to facilitate regulatory evaluation and possible commercial adoption.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".