Chemical and Protein Quality of Soybean (Glycine max) and Tigernut (Cyperus esculentus) Based Weaning Food
Bibliographic record
Abstract
Malnutrition and poverty poses a major challenge to low-income families in developing nations and the twin issues are very critical for a growing infant. Commercially processed weaning foods are expensive for these categories of families; hence the objective was to formulate and evaluate the functional, antinutritional and protein quality of composite weaning food based on soybean and tigernut flour. Soybean and Tigernut seeds were processed into flour and three weaning diets: STF1 (Tigernut; 75%, Soybean 15%); STF2 (Tigernut; 65%, soybean 25%) and STF3 (Tigernut; 55%, Soybean 35%) with 10% full cream milk powder (FCM) addition were produced according to FAO/WHO/UNU recommendations. Commercial weaning food (CB) was used as control. Effect of tigernut flour addition on the functional, antinutritional and protein quality of the formulated blends were evaluated using standard methods. The protein quality was evaluated using rat assay. The functional properties of the samples were significantly (P<0.15) different from the commercial sample. STF3 sample had significantly (P<0.05) lower swelling index (SI), Packed bulk density, (PBD), loose bulk density (LBD) and water absorption capacity (WAC) with values of 3.63±0.10, 0.53±0.01 g/cm, 0.32±0.03 g/cm and 187.00 ± 2.10 ml/100g compared to values of 6.14±0.22, 0.55 ± 0.01g/cm, 0.42±0.01g/cm and 374.00 ± 3.40 for commercial sample, respectively. Total oxalic, soluble oxalic acid, phytic acid and tannins values of the diets were significantly (P<0.05) higher than CB, and the lowest values were for STFS. Protein quality indices of the samples showed significant (P<0.05) difference. The NPU, PER, NPR, TD and BV of STF3 compared favorably with CB. STF3 sample supported good growth for the growing rats. The results suggested that STF3 is nutritionally balanced & possessed good growth promoting quality for a growing infant and could be adopted at the house hold-level to curb infant malnutrition and death.
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How this classification was reachedexpand
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 | 0.003 | 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 teacher head, 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".