Methods for the Control of Foodborne Pathogens in Low-Moisture Foods
Bibliographic record
Abstract
) less than 0.85 and are generally considered less susceptible to microbial spoilage and the growth of foodborne pathogens. However, in recent years, outbreaks linked to LMFs have increased, with Salmonella spp., Bacillus cereus, Cronobacter sakazakii, Clostridium spp., Escherichia coli O157:H7, non-O157 E. coli, and Staphylococcus aureus being the principal pathogens involved. Because of the new concerns raised as a result of recent outbreaks, new approaches need to be developed to control foodborne pathogens in LMFs. This review summarizes the recent research on novel inactivation methods suitable for use on LMFs. Among the methods discussed are the nonthermal inactivation methods as well as other novel methods such as radio-frequency and microwave heating. Additional research is needed to evaluate older technologies and develop new technologies, either alone or in combination, to understand the mechanisms of inactivation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".