Assessment of Quality and Safety of Winged Termites (Macrotermes bellicosus) Enriched Locally Formulated Complementary Foods
Bibliographic record
Abstract
Addition of edible insects to local staples used as complementary foods can improve their nutrient content. Nutritional quality and safety of Macrotermes bellicosus enriched boiled rice (BR) and yam (BY) complementary foods (CFs) was assessed using rats. Macrotermes bellicosus (MB) were collected, dried, and refrigerated at -4oC. Ground MB was added to BR and BY in ratios 10.0%, 15.0%, 20.0% (w/w) to give BR1, BY1; BR2, BY2, and BR3, BY3 respectively. Nutrient content of MB, BY, BR and MB-enriched CFs were determined by AOAC methods. Nutrient bioavailability and safety of BR3 and BY3 were assessed using rats fed ad libitum for 28 days. Serum trace minerals in the CFs, control and basal diets and histopathological effects of CFs on rats’ organs were determined. Data were analysed using ANOVA at p<0.05. Dried MB contained 31.8g protein, 16.4g fat, 3.8g ash, 227.5mg calcium, 2.1mg iron, 15.0mg zinc, 330.4μg retinol equivalent (RE), and 529.0kcal energy/100g sample. The BR and BY contained 3.7-5.9g protein, 70.0-120mg calcium,4.2-5.6mg iron, 1.2-1.5mg zinc and 380- 386kcal/100g compared with 7.9-15.3g protein, 242.2-264mg calcium, 2.4-4.4mg iron, 15.1-19.8mg zinc and 357-372kcal/100g enriched CFs (p<0.05). Rats Serum trace minerals ranged between 3.4- 4.3mg zinc, 23.4-27.9mg calcium, 30.6-37.0mg iron; and 52.5-56.9μg RE, compared with control (3.2, 22.2, 34.1, 48.2) and basal (2.2, 21.1, 24.0 mg, 32.3 μg) diets respectively (p<0.05). No pathological lesions were observed in internal organs of rats on CF diets. Adding Macrotermes bellicosus to local complementary foods is safe and improved their nutritional quality, hence its use is recommended among mothers.
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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 | 0.000 |
| 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) | 0.001 | 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".