Pulsed-light inactivation of pathogenic and spoilage bacteria on cheese surface
Bibliographic record
Abstract
Cheese products are susceptible to postprocessing cross-contamination by bacterial surface contamination during slicing, handling, or packaging, which can lead to food safety issues and significant losses due to spoilage. This study examined the effectiveness of pulsed-light (PL) treatment on the inactivation of the spoilage microorganism Pseudomonas fluorescens, the nonenterohemorrhagic Escherichia coli ATCC 25922 (nonpathogenic surrogate of Escherichia coli O157:H7), and Listeria innocua (nonpathogenic surrogate of Listeria monocytogenes) on cheese surface. The effects of inoculum level and cheese surface topography and the presence of clear polyethylene packaging were evaluated in a full factorial experimental design. The challenge microorganisms were grown to early stationary phase and subsequently diluted to reach initial inoculum levels of either 5 or 7 log cfu/slice. White Cheddar and process cheeses were cut into 2.5×5 cm slices, which were spot-inoculated with 100 µL of bacterial suspension. Inoculated cheese samples were exposed to PL doses of 1.02 to 12.29 J/cm(2). Recovered survivors were enumerated by standard plate counting or the most probable number technique, as appropriate. The PL treatments were performed in triplicate and data were analyzed using a general linear model. Listeria innocua was the least sensitive to PL treatment, with a maximum inactivation level of 3.37±0.2 log, followed by P. fluorescens, with a maximum inactivation of 3.74±0.8 log. Escherichia coli was the most sensitive to PL, with a maximum reduction of 5.41±0.1 log. All PL inactivation curves were nonlinear, and inactivation reached a plateau after 3 pulses (3.07 J/cm(2)). The PL treatments through UV-transparent packaging and without packaging consistently resulted in similar inactivation levels. This study demonstrates that PL has strong potential for decontamination of the cheese surface.
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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 | 0.000 |
| 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 | 0.000 |
| 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".