Rapid, non-destructive and comprehensive quantitative analysis of honey by combined use of conventional and broadband-WET NMR spectra
Bibliographic record
Abstract
High concentrations of major components in honey-fructose, glucose, and water-often hinder detection of minor components that define its nutritional and medicinal properties. An improved broadband-WET NMR method effectively suppressed signals from these dominant components, enabling rapid identification and quantification of minor constituents without separation. Analysis of seven honey types identified 33 components, 22 of which were quantified. Acacia honey showed the highest fructose/glucose ratio, whereas buckwheat honey contained the highest levels of branched-chain amino acids and γ-aminobutyric acid. Fresh polyfloral honey was monitored for six months at 25 °C and 37 °C. Tryptophan content halved at 37 °C but remained stable at 25 °C. The known aging indicator 5-(hydroxymethyl)furfural increased only at 37 °C, while 3-deoxyglucosone rose at both temperatures, about three times faster at 37 °C. This non-destructive NMR approach enhances honey quality evaluation and storage assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".