Complementary molecular methods detect undeclared species in sausage products at retail markets in Canada
Bibliographic record
Abstract
Accurate food labelling is of utmost importance for food safety and consumer choice in the food chain. Complete or partial substitution, whether intentional or unintentional, may introduce food pathogens or allergens to a product or affect personal or religious beliefs. Several studies around the world have reported different degrees of species substitution in meat products but no similar studies have been conducted in the Canadian market for sausage products. In this study, 100 raw meat sausage samples that were labelled as single meat species products (beef, pork, chicken or turkey) were collected from retail establishments across Canada and were surveyed for the presence of a panel of non-labeled species. The predominant meat species were determined using DNA barcoding and contaminant or unclaimed meat species were detected using digital droplet PCR using species specific primers and probes. All samples were also tested for presence of horse meat using real-time PCR. All samples contained the predominant species matching the label species except for five turkey sausage samples which contained chicken as the predominant species. Second, this analysis showed that 6% of beef sausages also contained pork, 20% of chicken sausages contained turkey while 5% contained beef, and 5% of pork sausages also contained beef. Five samples labeled as turkey sausage contained no turkey and one pork sample was found to contain horse meat. The overall mislabeling rate detected in this study was 20% and the results provide a baseline for assessing species mislabeling in processed meat products in Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".