Discrimination of the geographic origin of pork using multi‐isotopes and statistical analysis
Bibliographic record
Abstract
Rationale While global pork production has grown exponentially in recent decades to 109 Mt in 2010, methods aimed at verifying the geographic origin of pork products have yet to be thoroughly investigated. Here, we analyzed pork samples available in South Korea in order to discriminate their geographic origin. Methods A total of the 37 pork samples originated from South Korea and other countries (Denmark, Germany, France, Spain, Canada and Mexico) were collected in order to classify their geographic origins using multi‐isotope ratios, such as δ 18 O, δ 2 H, δ 13 C, δ 15 N values measured by IRMS, 87 Sr/ 86 Sr ratios measured by MC‐ICP‐MS, and multivariate statistical approaches. Results There is a wide range of 87 Sr/ 86 Sr ratios in the pork samples, varying from 0.70779 to 0.71245, due to the lithology where the pork was raised. Canadian samples displayed the lowest δ 18 O and δ 2 H values mainly due to the latitude effect. Furthermore, the δ 13 C values of European and Canadian samples were lower than those of Korean and Mexican samples, depending on whether the feed was composed of either C 3 or C 4 plants. The δ 15 N values of the European and Canadian samples were much higher than those of the other samples, possibly resulting from the δ 15 N values of the feed. Conclusions While differences in pork samples were observed that depended on geographic origin, this study suggests that more detailed investigations are needed to validate whether a combination of multi‐isotope and multivariate statistical approaches is a valid method for determining the geographic origin of pork.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".