Factors affecting the inactivation of the natural microbiota of milk processed by pulsed electric fields and cross-flow microfiltration
Bibliographic record
Abstract
Prior to processing milk and cream were standardised and homogenised. Skim milk was cross-flow microfiltered (CFMF) prior to treatment with pulsed electric fields (PEF) or high temperature short time (HTST) pasteurization. The effect of temperature of the skim milk and product composition on the efficacy of PEF treatment was determined. The electrical conductivity of the product was related to fat and solids content and increased 5% for every g/kg increase of solids and decreased by nearly 0·7% for every g/kg increase of fat. From the three microbial groups analyzed (mesophilic, coliform, and psychrotroph) in milks differences (P<0·05) in the inactivation of mesophilic microorganisms were observed between the counts following PEF treatment, while HTST pasteurization resulted in higher reductions in all different counts than those obtained after PEF. Increasing the skim milk temperature prior to PEF treatment to about 34°C showed equivalent reductions in microbial counts to skim milk treated at 6°C in half the time. The reductions achieved by a combination of CFMF and PEF treatments were comparable to those achieved when CFMF was combined with HTST pasteurization. A higher reduction in coliform counts was observed in homogenised products subjected to PEF than in products that were only standardised for fat content.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".