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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it