A comprehensive characterization of phenolics, amino acids and other minor bioactives of selected honeys and identification of botanical origin markers
Bibliographic record
Abstract
Phenolic and amino acid profiles along with organic acid, vitamin and mineral contents, major and minor sugars and enzyme activities of selected honey samples collected in North America were analyzed using different methods and potential markers of their botanical origin were identified. Among the 29 detected phenolic compounds, and some were found to be a good chemical markers to distinguish a genuine honey given its propolis origin. Quantitative data and principal component analysis showed that hesperidin, caffeic acid/isoferulic acid, and p-hydroxybenzoic acid/p-coumaric acid have the most positive relationship to the orange, alfalfa, and buckwheat honey, respectively, indicating their potential roles as chemical markers of these floral honeys. Free amino acid profiles were similar in all other honeys except buckwheat which not only had significantly higher branched-chain amino acids but was the only floral honey that contained L-norvaline that was identified for the first time. The enzyme activities and the major and rare sugar composition helped explain the presence of the various organic acids in the honeys. Compositional data of these bioactives and other nutrients will not only serve as database information for honey derived from North America but also provide insightful knowledge for the underlining potential health benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".