Evaluation of thermal destruction kinetics of <i>Clostridium difficile</i> spores (ATCC 17857) in lean ground beef with first‐order/Weibull modeling considerations
Bibliographic record
Abstract
Abstract Thermal destruction kinetics of spores of Clostridium difficile ATCC 17857 was evaluated between 74 and 82°C and characterized using the first‐order log‐linear and Weibull models. Computed decimal reduction times using the first‐order model ranged from 4.39 min at 82°C to 146 min at 74°C, with a z value of 5.17°C. Thermal destruction data were also analyzed using the Weibull model. Based on regression, the predicted one‐D value (first‐order model) and the reliable life (tR) (Weibull model) were 3.86 and 4.05 min at 82°C and 136 and 165 min at 74°C, respectively, indicating the Weibull model to be more conservative yielding higher decimal reduction time values. However, when extended to achieve 2.5, 4, and 6 decimal reductions in C. difficile spores, the calculated process times were more conservative with the first‐order model than with the Weibull model. Moreover, within the experimental range, when data for both models could be compared, predictions from the first‐order model were much closer to the experimental values. Therefore, when used for process calculation for 2.5 or higher log reductions, the first‐order model would give more conservative and safer process times. The study provides destruction kinetics data for C. difficile under a range of temperature conditions. Practical Applications Clostridium difficile is a major cause of antibiotic‐associated diarrhea and pseudomembranous colitis in humans. C. difficile infection is the leading cause of gastroenteritis‐associated death. C. difficile infections have been increasing in recent years, and therefore warrant appropriate remedial measures. Thermal inactivation is the most common method for pathogen control, and often the cooking practices are adjusted to a level pre‐established to make the foods pathogen free. Available information on thermal destruction kinetics is scarce, and therefore the data generated here should be of significant importance for safety considerations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".