Comparative Study on Nutrient Composition, Functional Property and Glycaemic Index of “Ogi” in Healthy Rats Prepared from Selected Cereal Grains
Bibliographic record
Abstract
One of the most common traditional fermented food products consumed by all class of people in Southern part of Nigeria is Ogi. Ogi is a gurel made from different types of cereal ranging from maize, sorghum, millet among others. Hence, this study was aimed to evaluate nutrient, phytochemicals, functional properties, and glycemic index of Ogi produced from different grains. Grains such as yellow maize (YM), white maize (WM), popcorn maize (PC), red sorghum (RS), white sorghum (WS) and finger millet (FM) were processed into Ogi samples, packaged in airtight container prior to analysis and stored at –20 °C. Proximate results showed that Ogi sample produced from PC significantly contained the highest protein content followed by sample YM. Potassium was observed to be the most abundant mineral elements in Ogi (14.50–19.10 mg/100 g). The phytochemical composition (mg/g) of the experimental samples ranged from 0.34–1.62, 53.82–177.09, 0.00–0.10, 3.71–69.22 and 2.08–6.08 mg/g in oxalate, saponin, flavonoid, phytate and tannin, respectively. The glycemic index ranged 48.83% in YM to 50.71% in WS, while the glycemic load ranged 31.96% in YM to 37.82% in WS, respectively. The result revealed that Ogi samples, especially YM was high in calcium, Ca/P ratio, iron, zinc but low in phytate and tannin content. Sample YM also exhibit lower glycemic index, when compared with other Ogi samples. Hence, YM may be a better grain for the production of Ogi samples.
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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) | 0.001 | 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 | 0.001 |
| 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".