Comparing the health risks of alfalfa sprouts and wheatgrass via detecting the presences of escherichia coli in their juices
Bibliographic record
Abstract
Background: Past studies have analyzed the health risks associated with alfalfa sprout production and developed standard procedures to reduce foodborne illnesses. There have been no studies related to microgreen outbreaks, specifically wheatgrass. Wheatgrass has become a growing culinary trend and the potential health risks associated need to be evaluated. Alfalfa sprouts and wheatgrass both share the same initial growth production – pre-soak and germination. The only difference is the harvesting period. This paper evaluated the risks associated with alfalfa sprout production and compared it with wheatgrass production by contaminating both alfalfa sprouts and wheatgrass with E. coli The presences of E. coli in the plant’s juices were evaluated and compared. Method: Alfalfa sprouts and wheatgrass were grown in similar conditions, in hydroponic condition, with an additional wheatgrass in soil. The plants were grown and harvested according to its respective pre-soaking and harvesting period, as specified by the Canadian Food Inspection Agency. The plants were inoculated with Escherichia coli during the germination period, and then juiced to examine the presences of E. coli within its internal structure. The Hygiena systemSURE II luminometer was used to detect the presences of E. coli via the MicroSnap™ Enrichment and E. coli detection swabs. Results: The result showed that E. coli was present in both wheatgrass and alfalfa sprouts juice. The root systems of the food products were independent of each other. The types of growth medium used for wheatgrass were also independent of each other. Conclusion: The study found that growing microgreens should be treated similarly to sprout productions. Food facilities with wheatgrass production need to be aware of safe handling, production, and storage of wheatgrass to prevent foodborne illnesses.
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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 | 0.000 | 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 | 0.000 | 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".