Physicochemical Characteristics and Microbiological Quality of Honey Produced in Benin
Bibliographic record
Abstract
Honey is a very complex biological product. It has great diversity, giving it a multitude of properties, both nutritionally and therapeutically. This study aimed to study the physicochemical and microbiological characteristics of honeys collected during the dry and rainy seasons in the different phytogeographical areas of Benin. The study revealed that all honeys had pH, water content, electrical conductivity, ash content, free acidity, total sugars, and reducing sugars, respectively, ranging within 3.65–4.09; 12.07–13.16%; 530.25–698.50 μs/cm; 0.42–0.53%; 35.67–40.52 meq/kg; 60–70%; and 58–70%. Moisture content, total sugars, and reducing sugars varied very significantly ( p<0.05 to p<0.001 ) from one area to another and from one season to another. However, only the production season has a significant influence ( p<0.05 ) on the pH of the honey. With regard to the ash content, free acidity, and electrical conduction, no significant difference ( p>0.05 ) between the zones or between the seasons was observed. The results of the microbiological characterization showed that there is heterogeneity in the microbial load. These results have shown that these honeys meet international standards and their characterization will make it possible to obtain Beninese quality labels.
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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 | 0.001 | 0.001 |
| 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".