Recent development in low-moisture foods: Microbial safety and thermal process
Bibliographic record
Abstract
Foodborne outbreaks and recalls of pathogen-contaminated low-moisture foods (LMFs, foods with water activity at 25 °C < 0.85) have led to numerous scientific studies on bacterial persistence, as well as newly developed industrial interventions. Conducting microbial tests of LMFs, lab tests, or validation studies in pilot plans requires complete information on protocols and parameters that need to be aware of-in particular, understanding how factors influence the thermal resistance of bacterial pathogen in LMFs is critical in designing any thermal processes. This review provides detailed information on the general protocols of microbial studies of LMFs: from pertinent pathogen identification to microbial validation studies. In particular, it reviewed the detailed procedures (e.g., lawn-harvest method), analytical protocols (e.g., recovery and enumeration of pathogens in LMFs), and specialized tools that have been utilized (even widely accepted) in laboratory-based microbial studies of LMFs. It also summarized the factors that influence the microbial validation studies. This article could support the intervention of existing pasteurization processes in the LMF industry, promoting the microbial safety of LMFs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".