Amino acid profiles and in vitro antioxidant properties of cereal-legume flour blends
Bibliographic record
Abstract
Legumes and cereals stand a great chance as remedies to overcome incidences of protein malnutrition and oxidative stress. Cereal grains such as wheat (WT), millet (ML), maize (MZ) and acha (AC) were blended with cowpea (CP), peanut (PN) and soybean (SO) at varying levels to produce four different blends. Protein contents of the cereal-legume blends were determined and found to be higher as the inclusion level of legumes increased in the blends. Amino acid profiles and antioxidant properties of the flour blends were evaluated. Results showed that leucine was the most abundant (6.52-8.45 g/100 g) essential amino acid in the flour blends while total content of essential amino acids increased as the level of legume incorporation increased in the WT/SO (31.3-36.2 g/100 g) and MZ/CP (34.5-37.4 g/100 g). The antioxidant properties showed that MZ50:CP50 exhibited greater ferric reducing antioxidant power while WT70:SO30 and AC50:SO50 had stronger metal chelation activity and ML50:PN50 scavenged the most DPPH radicals when compared to the other flour blends. The results suggest that the composite flours have the potential to be used as ingredients for the formulation of food products with high levels of essential nutrients in addition to antioxidant benefits.
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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 | 0.000 | 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 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".