The Physicochemical Parameters, Phenolic Content, and Antioxidant Activity of Honey from Stingless Bees and Apis mellifera: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The most common bee species used for honey production is Apis mellifera (A. mellifera), followed by stingless bees. This study included scientific articles using the PRISMA approach. A random effect model was implemented and the effect size (ES) was calculated and reported as the standardized mean difference (SMD) and raw mean difference (RMD). The mean phenolic content in A. mellifera honey was 61.21 ± 28.3 mg GAE/100 g and stingless bee honey +33.69 mg GAE/100 g; p = 0.01. The antioxidant activity, discovered by the Ferric Reducing Antioxidant Power (FRAP) method, showed a mean of 97.34 ± 7.84 μmol Fe(II)/100 g in A. mellifera and stingless bee honey +63.39 μmol Fe(II)/100 g; p = 0.009. The physicochemical properties showed significant differences in moisture (A. mellifera honey 19.54 ± 3.65%; stingless bee honey +8.02%; p = 0.0001), hydroxymethylfurfural (HMF) (A. mellifera honey 20.14 ± 16.27 mg/kg; stingless bee honey −11.25 mg/kg; p = 0.001), and free acidity (A. mellifera honey 31.32 ± 16.67 meq/kg; stingless bee honey +34.76 meq/kg; p = 0.01). The variability in the trials was explained by the heterogeneity, and a meta-regression analysis incorporated four covariates: (1) stingless bee species; (2) floral source; (3) country, and (4) latitude. This study highlights the importance of conducting further studies on stingless bee 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".