Peracetic Acid Efficacy and Decay Kinetics in Poultry Processing under Chiller Conditions
Bibliographic record
Abstract
Abstract Pathogens on poultry products continue to pose critical public health risks. Despite significant literature examining sanitizer impact on bacterial pathogens during chilling, the mechanisms of sanitizer efficacy in terms of pathogen and organic load, sanitizer levels, and exposure duration are not well understood. To assess these, we report on experimentally-informed-mechanistic-modeling to describe pathogen dynamics during poultry chilling. The shedding and survival of a five-strain cocktail of poultry-plant derived Salmonella enterica serovars, at high and low loads, with exposure to peracetic acid (PAA; 0 – 200 mg·L -1 ) for up to 60 min in 10-L chiller tanks, in the presence or absence of whole chicken carcass/parts, were measured. Process water parameters versus time were simultaneously monitored. Results suggest that total dissolved solids (TDS) predict PAA decay more consistently than chemical oxygen demand (COD). A mathematical model for PAA decay and pathogen shedding/inactivation was developed. This model accurately predicted PAA level changes in the pre/main chiller of a high-speed poultry processing plant in North America. Without PAA, Salmonella shedding from chicken thighs is influenced by rinse time and number of rinses. Without organic load, residual PAA (1 mg·L -1 ) inactivated bacteria given sufficient exposure time, although PAA levels > 5 mg·L -1 were essential for rapid inactivation. With organic load, initial PAA concentration (> 40 mg·L -1 ) and exposure time (> 2 min) were critical for bacterial inactivation, with model results connecting process conditions to dominate modes of bacterial inactivation on chicken. The insights from such experimental-modeling studies provide key tools for processors to improve pathogen control during chilling.
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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 | 0.001 | 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".