Experimental and numerical study of heterogeneous pressure‐temperature‐induced lethal and sublethal injury of <i>Lactococcus Lactis</i> in a medium scale high‐pressure autoclave
Bibliographic record
Abstract
The present contribution is dedicated to experimental and theoretical assessment of microbiological process heterogeneities of the high-pressure (HP) inactivation of Lactococcus lactis ssp. cremoris MG 1363. The inactivation kinetics are determined in dependence of pressure, process time, temperature and absence or presence of co-solutes in the buffer system namely 4 M sodium chloride and 1.5 M sucrose. The kinetic analysis is carried out in a 0.1-L autoclave in order to minimise thermal and convective effects. Upon these data, a deterministic inactivation model is formulated with the logistic equation. Its independent variables represent the counts of viable cells (viable but injured) and of the stress-resistant cells (viable and not injured). This model is then coupled to a thermo-fluiddynamical simulation method, high-pressure computer fluid dynamics technique (HP-CFD), which yields spatiotemporal temperature and flow fields occurring during the HP application inside any considered autoclave. Besides the thermo-fluiddynamic quantities, the coupled model predicts also the spatiotemporal distribution of both viable (VC) and stress-resistant cell counts (SRC). In order to assess the process non-uniformity of the microbial inactivation in a 3.3-L autoclave experimentally, microbial samples are placed at two distinct locations and are exposed to various process conditions. It can be shown with both, experimental and theoretical models that thermal heterogeneities induce process non-uniformities of more than one decimal power in the counts of the viable cells at the end of the treatment.
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 | 0.000 |
| 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 | 0.001 | 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".