Mapping the global origins of soybean: a study using ICP-MS and chemometrics
Bibliographic record
Abstract
To enhance transparency in the soybean supply chain and help prevent misrepresentation of geographic origin, an analytical method combining ICP-MS with chemometrics was developed. A total of 422 soybean samples were collected from Brazil, the United States, Argentina, China, India, Paraguay and Canada, representing over 95% of global production. The OPLS-DA multivariate analysis model used for classification achieved 98.5% accuracy, with Ni, Na, Mo, Ba, Co, Cr, Cd, Sr, Se, K and Ca identified as key elements for origin differentiation. This approach provides a practical tool for companies and regulators to verify geographic origin, supporting compliance with trade and sustainability requirements and tariff-related controls. Additionally, the ability to differentiate soybean samples from various regions within Brazil and the United States was investigated and preliminary comparisons of meal samples from deforested and non-deforested areas in Brazil revealed elemental differences, suggesting potential environmental influences and highlighting the need for further investigation.
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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 | 0.002 | 0.003 |
| 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) | 0.000 | 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".