Analytical strategies for sensitive and precise determination of 87Sr/86Sr in olive oil through ion extraction, chromatographic separation, and multicollector inductively-coupled plasma mass-spectrometry
Bibliographic record
Abstract
Several food regulatory bodies regard olive oil as highly susceptible to food fraud, largely due to its substantial economic worth. Precise analytical tools are being developed to uncover these types of fraud. This study examines an innovative approach to extract strontium (Sr) from the olive oil matrix (via EDTA complexation and ion-exchange chromatography) and to determine its isotope composition by MC-ICP-MS. This technique was compared to a commonly used technique (i.e. acid extraction and extraction chromatography), and then validated. Three olive oils that are sold in France were prepared and analyzed by two methods: 1) acid extraction prior to Sr purification by Sr-spec resin and 2) complexation by EDTA prior to Sr purification by AG50W-X8. These methods were applied for the determination of the 87Sr/86Sr isotope ratio of 23 olive oils from various countries. We also demonstrated the feasibility of the method for the detection of olive oil mixtures.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".