Impact of processing steps (filtration, creaming and pasteurization) on the botanical classification of honey using LC-QTOF-MS
Bibliographic record
Abstract
The present study investigated the impact of filtration, creaming and pasteurization on the authentication of the botanical origin of honey using the dilute-and-shoot method in liquid chromatography coupled to mass spectrometry (LC-MS). The analytical method performances were satisfactory (analyte recoveries ranging from 95 % to 103 % and inter-day precision below 12 %). Three types of raw honeys including blueberry, canola and clover were processed under controlled conditions. Filtration, creaming and pasteurization had no impact on honey botanical classification based on the LC-MS fingerprint, and the key molecular fingerprints were retained after processing. However, results revealed that testing the impact of processing is essential when selecting honey authenticity markers because some candidates (e.g. adenosine) are not stable or can be removed during honey processing. The results of the present study also highlighted the suitability of the dilute-and-shoot approach to both develop authentication tools for honey and study the impact of processing methods on specific chemicals in honeys.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".