Effect of High‐Pressure Processing Operating Parameters on Microbial Inactivation and Bioactive Protein Preservation in Bovine Milk: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it