Biochemical Relevance of Sorghum and Millet Produced In the Kasena-Nankana Districts of Ghana, And Some of Their By-Products to Food, Nutrition, Health and Wealth of the People
Bibliographic record
Abstract
This study was conducted in two farming seasons between 2008 and 2010. Two varieties each of millet and sorghum from the Kasena-Nankana districts of the Upper East Region of Ghana were compared. Methods used were personal interviews, proximate and micronutrient analyses, and assessment of “waste products”. Results show that Pearl millet:finger millet:red sorghum:white sorghum are used respectively to make 5: 8:5:5 diets. For the first season, the highest average contents were: Crude protein(12.2%), and fibre(5.4%) in the white sorghum; carbohydrates(83.1%) in the proso millet, fat(4.2%) and ash(1.6%) in the pearl millet and moisture range from 0.3-2.1%. Also the red sorghum had the highest average of Na(548.5 mg/kg), Fe(68.8 mg/kg), and Mg(509.3 mg/kg); K(668.0 mg/kg) was highest in both red sorghum and pearl millet; Mn(16.5 mg/kg) in the pearl millet, and Ca(29.3 mg/kg) in the proso millet. In the second season, the highest average amount of crude protein(14.4%), fat(2.9%), ash(2.1%) and fibre(3.5%) were in the white sorghum, and carbohydrates(76.2%) in the red sorghum. The working moisture ranged from 8.2-17.7%. The highest average Na, Fe, and Mg were respectively 509.0 mg/kg, 70.5 mg/kg, 540.2 mg/kg all in the red sorghum. K(690.0 mg/kg) and Mn(12.8 mg/kg) were in the pearl millet, while Ca(32.0 mg/kg) was in the proso millet. The aqueous extract of the stalk ashes of pearl millet were used to flavour and de-ferment soups; stalks of sorghum served as cooking fuel, and for making baskets and sleeping mats, and the husk as cattle feed. Dusa and pito mash are food by-products from the grains used in feeding poultry and non-ruminants. Dusa had highest crude protein(21.48%) and carbohydrates(51.48%) and pito mash had 9.30% fat, fibre(11.38%) and ash(5.97%).
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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 | 0.001 | 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) | 0.002 | 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".