Unleashing the antimicrobial potential of high-pressure processing on beverages, sauces, purées, and milk: A predictive modelling approach
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
High-pressure processing (HPP) is of great interest due to the nutritive and environmental concerns arising from thermal treatments. The aims of this review were to i) evaluate the efficiency of HPP on the viability of pathogenic and spoilage-causing microorganisms ii) assess the improvement in shelf life that could occur because of HPP and iii) create a model for predicting microbial reduction in milk/juices/other beverages. A literature search was performed on articles appearing prior to March 12, 2023, using Scopus and PubMed. A regression model using a forward selection technique was applied to predict the microbial reductions. Under optimal processing conditions of pressure, temperature and time, with ideal food characteristics, including good chemical stability, no entrapped air, low pH, and high-water activity (a w ), when packed in low gas permeable flexible film under refrigeration, liquid product shelf lives of ≤ 120 d can be anticipated. Gram-negative bacteria and viruses were less resistant towards HPP than Gram-positive bacteria by about 1 log. Microbial reduction in orange juice was greater than in apple juice or milk. HPP followed by refrigeration or freezing or HPP combined with gallic acid/essential oils/aged green tea extract/dimethyl dicarbonate had a greater lethal effect on microorganisms than when HPP was used alone. Inadequate pressure/time parameters may result in sub-lethally injured microorganisms capable of survival. HPP did not kill bacterial spores. Therefore, HPP-treated low-acid foods must be stored and distributed refrigerated to prevent germination and outgrowth by bacterial spores/ sub lethally injured cells. The combination of other additives along with HPP may result in greater microbial reductions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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