Chemical Composition and Antioxidant Profile of Sorghum (Sorghumbicolor (L.) Moench) and Pearl Millet (Pennisetumglaucum (L.) R.Br.) Grains Cultivated in the Far-North Region of Cameroon
Bibliographic record
Abstract
Sorghum and pearl millet are grain crops that can grow in semi-arid climates, with nutritional and bioactive properties superior to those of major cereals such as rice, wheat, and maize. However, these properties vary a lot, depending on the genetic factors, growing conditions, and place of cultivation. Four sorghum and two pearl millet grains cultivars grown in the Far-North region of Cameroon were screened for their chemical composition and antioxidant profile. The proximate and mineral analyses were performed using AOAC standard methods. The antioxidant profile was assayed spectrophotometrically and details on the phenolic compounds were investigated using HPLC. The pearl millet cultivars, especially mouri, showed higher contents of proteins, lipids, ash, calcium, copper, iron, and zinc. The red sorghum specifically exhibited the greatest amounts of total polyphenols (82.22 mg GAE/g DE), total flavonoids (23.82 mg CE/g DE), and total 3-deoxyanthocyanidin (9.06 mg/g DE). The most abundant phenolic compound was gallic acid, while the most frequent were chlorogenic and ferulic acids. The maximum antioxidant activity against DPPH was observed in yellow-pale sorghum (87.71%), followed by red sorghum (81.15%). Among the studied varieties of cereals, mouri pearl millet and red sorghum were the best sources of nutrients and bioactive compounds, respectively. Their consumption should be encouraged to tackle nutrient deficiencies and non-communicable diseases within local populations.
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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 | 0.000 | 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 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".