Practice and Progress: Updates on Outbreaks, Advances in Research, and Processing Technologies for Low-moisture Food Safety
Bibliographic record
Abstract
Large, renowned outbreaks associated with low-moisture foods (LMFs) bring to light some of the potential, inherent risks that accompany foods with long shelf lives if pathogen contamination occurs. Subsequently, in 2013, Beuchat et al. (2013) noted the increased concern regarding these foods, specifically noting examples of persistence and resistance of pathogens in low-water activity foods (LWAFs), prevalence of pathogens in LWAF processing environments, and sources of and preventive measures for contamination of LWAFs. For the last decade, the body of knowledge related to LMF safety has exponentially expanded. This growing field and interest in LMF safety have led researchers to delve into survival and persistence studies, revealing that some foodborne pathogens can survive in LWAFs for months to years. Research has also uncovered many complications of working with foodborne pathogens in desiccated states, such as inoculation methods and molecular mechanisms that can impact pathogen survival and persistence. Moreover, outbreaks, recalls, and developments in LMF safety research have created a cascading feedback loop of pushing the field forward, which has also led to increased attention on how industry can improve LMF safety and raise safety standards. Scientists across academia, government agencies, and industry have partnered to develop and evaluate innovate thermal and nonthermal technologies to use on LMFs, which are described in the presented review. The objective of this review was to describe aspects of the extensive progress made by researchers and industry members in LMF safety, including lessons-learned about outbreaks and recalls, expansion of knowledge base about pathogens that contaminate LMFs, and mitigation strategies currently employed or in development to reduce food safety risks associated with 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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".