Functional Characterization of Scaptotrigona mexicana Honey: Physicochemical Properties, Antioxidant Capacity, and α-Amylase Inhibition for Food Process Applications
Bibliographic record
Abstract
For centuries, Scaptotrigona mexicana honey has been treasured in Mexico, where pre-Columbian cultures harvested it not only for its sweet flavor but also for its medicinal and ceremonial purposes. Today, it remains a high-value product in local markets, prized above Apis mellifera honey for its unique sensory qualities and traditional health benefits. Yet its scientific characterization and functional potential remain underexplored. In this study, twenty-four samples from diverse regions were analyzed to quantify bioactive compounds and determine physicochemical composition, α-amylase inhibition, and antioxidant activity. Non-parametric statistical tests revealed distinct compositional clusters, with samples from Cruz Blanca showing exceptional phenolic content and stronger α-amylase inhibition (5.6–49.2%). Antioxidant capacity correlated positively with phenols and flavonoids, showing moderate effect sizes for ABTS (η2 = 0.49) and DPPH (η2 = 0.37). Compared with Apis mellifera honey, Scaptotrigona mexicana contained more moisture, free acidity, phenols, and antioxidants, but less diastase, hydroxymethylfurfural, and reducing sugars. Importantly, natural α-amylase inhibitors can help modulate postprandial glucose, offering dietary support for type 2 diabetes management. Kinetic analyses (EC50, Vmax, and Km) suggested mixed inhibition. These findings highlight Scaptotrigona mexicana as both a heritage product and a promising functional ingredient for developing foods that merge tradition with metabolic health innovation.
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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".