Ultrasonic and microwave technologies are promising for decrystallizing bioactive-rich honey: effect on particle size, sugar and phenolic profiles of honey from different geographical origins in Jordan
Bibliographic record
Abstract
This study aimed to investigate the effects of ultrasound and microwave treatment on honey decrystallization, individual sugar composition and phenolic profiles of honey from seven different sites in northern Jordan. Ultrasonication conducted at low temperature resulted in significant reduction of mean particle size by 30–45% compared to the controls, for all honey varieties except one, in contrast to microwave processing. Moderate variations under 25% were evidenced for sugar contents and fructose-to-glucose ratio in some honeys after processing. The predominant phenolic compounds in the free phenolic extracts of sonicated, microwaved and control samples included rutin (honey varieties from Jaresh, Irbid, Seder, Hashmeih and Um Elyanabe’e), 3,4-dihydroxyphenyl ethanol (3,4-DPE) and rosmarinic acid (Mafraq honey), and 3,4-DPE and catechin (Agwar honey). In the total phenolic extracts of differently processed honey, the major phenolics were rutin (Jaresh, Irbid, Mafraq, Seder, Hashmeih, Um Elyanabe’e honeys), and 3,4-DPE and catechin (Agwar honey). Findings show that low temperature ultrasonication was more effective than microwaving for decrystallizing honey. The natural (poly)phenolic richness of honey products was largely retained, with minimal degradation after both treatments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".