Sampling Strategies Used to Determine the Microbiological Recovery in Beef Carcass during Slaughter Operations: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
The use of microbiological sampling to test beef carcasses for ensuring food safety is a critical activity that food manufacturers need to prioritize. Differences in sampling strategy may affect the quality of the results being reported, possibly leading to misinformed action. Moreover, failure to use an appropriate sampling strategy directly impacts the validity of study results. A systematic literature, covering the period 1965-2020, was conducted to identify sampling strategies used to determine the microbiological quality of beef carcasses in slaughter operations in North America, South America, the European Union, and Australia. Six electronic bibliographic databases were searched for beef microbiological studies in English. Two independent trained reviewers analyzed the full text of articles to assess the quality of the study methods. A total of 30 articles were included for a full review. The number of carcass sites sampled ranged from 1 to 7. Brisket (23/27, 85.2%), flank (17/27, 63%), rump (13/27, 48.1%), and neck areas (8/27, 29.6%) were most often sampled. Most studies described sample characteristics, such as slaughter step to be sampled, carcass sites, and sampling tools used for sampling, sampling frequency, microbiological testing, and handling of sample. Seven had very small sample sizes (10, 18, and 25 beef carcasses). In 13 studies, samples were randomly collected. Only eight reported conducting a power analysis to determine sample size. The average of overall alignment score across all studies with government regulations (except Latin American studies) was 77 points (maximum point was 100). The average score was 62 points in the United States, 78 points in Canada, 90 points in Australia, and 77 points in European countries. Two main sampling tools (swabbing or excision or both) were used in 29/30 studies, with most (24) using swabbing. Microbiological analysis of carcass samples was mentioned in 28/30 studies, 18 used standard plate count, ......
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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 | 0.036 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".