Modelling viability of <i>Listeria monocytogenes</i> in paneer
Bibliographic record
Abstract
Paneer is a fresh, soft ready-to-eat cheese that is susceptible to Listeria monocytogenes contamination, exemplified by product recalls in Australia, Canada, and the USA. Previous research demonstrates that L. monocytogenes grows in paneer, however there are no paneer-specific predictive models that quantify the effect of environmental conditions on L. monocytogenes viability. This study measured the viability of a five-strain cocktail of L. monocytogenes in freshly prepared paneer incubated at 4–40 °C. Growth rates were fitted with the extended Ratkowsky square root model, with growth rates ranging from 0.014 to 0.352 log10 CFU/h. In comparison with published models, only the ComBase L. monocytogenes broth model acceptably predicted growth (Bf = 1.01, Af = 1.12) versus the developed model. The influence of paneer pH (5.0–6.0) and storage temperature (41–45 °C) on L. monocytogenes growth at the upper temperature growth boundary was described using a logistic model. These models provide quantitative tools to improve the safety of paneer processing conditions, shelf-life estimation, food safety management plans, and risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".