Validation of FoodChek™ - Salmonella for Rapid Detection of Salmonella in Eggs, Derivative Products, and the Environment
Bibliographic record
Abstract
The FoodChek™ - Salmonella assay is an immunomagnetic lateral flow assay for the rapid detection (shorter than 24 h) of the most frequently isolated Salmonella (groups B-E) in eggs, egg-derivative products, and environmental surfaces. The FoodChek - Salmonella assay correctly identified 99.6% (239/240) of the samples tested in the matrix studied, and the statistical analysis of the method comparison study results demonstrated that it performs as well as U.S. culture-based reference methods. Ninety-nine percent of the 103 Salmonella strains tested belonging to serogroups B-E were detected during the inclusivity study. Concerning the exclusivity, 31 nontarget strains were tested. No cross-reactivity was observed in FoodChek - Salmonella assay enrichment conditions. In addition, the assay shows strong robustness, good stability, and consistency among lots. The present study proves that the assay is an effective tool for the rapid detection of Salmonella spp. in whole liquid eggs, liquid egg white (liquid egg albumen), shell eggs, dried whole eggs, dried egg yolks, and environmental surfaces as stainless steel, plastic, rubber, ceramic tiles, and sealed concrete.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".