Identification of botanical marker candidates for buckwheat honey using a non-targeted approach based on liquid chromatography coupled with high resolution mass spectrometry
Bibliographic record
Abstract
Novel authentication tools are needed to determine unambiguously the botanical or geographical origin of food products such as honeys. In this study, a non-targeted workflow was developed to discover and identify authenticity markers for buckwheat honey based on a ‘dilute-and-shoot’ approach using liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS). A PLS-DA model was built using data obtained for 147 honeys whose characteristics had been checked with an orthogonal method (pollen analysis). The resulting model was able to distinguish buckwheat honeys from 13 other types of honey with a sensitivity of 100 %. Thirteen molecular features were identified as candidate markers for buckwheat honey, including three confirmed threshold markers: 4-hydroxybenzaldehyde, 2-hydroxypyridine and hydroxyquinoline-3‑carbonitrile. 2-Hydroxypyridine and hydroxyquinoline-3‑carbonitrile were first time reported as botanical marker candidates for buckwheat honey, highlighting the capacity of this approach to discover and identify novel candidate markers of authenticity. The present analytical workflow can be used, alone or in combination with other analytical techniques, to strengthen honey authentication. • A non-targeted workflow was developed to discover and identify authenticity markers. • The LC-QTOF fingerprint of honeys can distinguish buckwheat honeys from other types. • Thirteen features were identified as candidate markers for buckwheat honeys. • 2-Hydroxypyridine was highlighted as a botanical marker for buckwheat honey.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".