Overview of Recent Events in the Microbiological Safety of Sprouts and New Intervention Technologies
Bibliographic record
Abstract
Abstract There has been an increasing trend in consumption of sprouts worldwide due to their widespread availability and high nutrient content. However, microbial contamination of sprouts readily occurs due to the presence of pathogenic bacteria in seeds; and the germination and sprouting process provide optimal conditions for bacterial growth. In recent years, there has been a rise in the number of outbreaks associated with sprouts. These outbreaks occurred mainly in the US, Canada, UK, as well as Europe. More recently in 2011, there were 4 sprout‐related outbreaks, with the Escherichia coli O104:H4 outbreak in Germany causing around 50 deaths and 4000 illnesses reported. On top of pathogenic E. coli , Salmonella spp. are often associated with sprout‐related foodborne disease outbreaks. The contamination of sprouts has become a worldwide food safety concern. Hence, this review paper covers the outbreaks associated with sprouts, prevalence and characteristics of pathogens contaminating sprouts, their survival and growth, and the source of these pathogens. Physical, biological, and chemical interventions utilized to minimize microbial risks in sprouts are also discussed.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".