An Improved Chemical Extraction Procedure for the Sr Isotope Analysis of Liquid Agrifood Samples Applied to Authenticating the Origin of Maple Syrups in Quebec (Canada)
Bibliographic record
Abstract
ABSTRACT Rationale We describe a new simplified sample preparation technique, based on dual chromatography for the analysis of 87 Sr/ 86 Sr in liquid agri‐food samples. We applied this approach for authenticating the origin of maple syrup products in Quebec, characterizing the 87 Sr/ 86 Sr of soil profiles from geologically distinct maple groves, including maple tree components and maple syrup products. Methods In our simplified technique, 3 mL of the organic liquids was poured into a 15‐mL centrifuge tube, diluted with 10 mL of Mill‐Q H 2 O, and manually shaken to produce a thin juice. The thin juice was subsequently loaded in two 7.5 mL aliquots onto 10‐mL Bio‐RadTM chromatography columns containing 4 mL of cleaned AG50‐X8 resin (100–200 mesh in a 1‐N HCl solution). Once the sample was absorbed by the resin, the organic fraction of the thin juice was eluted by adding 3 × 5 mL of 1‐N HCl. The Sr and other cations were subsequently recovered by adding 2 × 5 mL of 6‐N HCl. Results The maple groves and the 87 Sr/ 86 Sr ratios for maple syrup from 39 different producing areas in Quebec indicated that no isotope fractionation occurs between the syrup, the maple trees, and the corresponding labile fraction of the soil they grew upon. This suggests that 87 Sr/ 86 Sr provides a reliable isotope fingerprint for the provenance of maple syrups. Gathering available agri‐food 87 Sr/ 86 Sr data across the Quebec province, we constructed the first bioavailable 87 Sr/ 86 Sr map based on actual agri‐food data. Conclusions This study reported an improved method for Sr separation for 87 Sr/ 86 Sr studies in liquids by combining two different cation‐exchange chromatography steps. Data were used to develop a bioavailable 87 Sr/ 86 Sr map that can be used to predict the geographical origin of agrifood products from southern Quebec.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".