Evaluation of Protein Nutritional Quality of Four Soybean Varieties Grown in Western Kenya
Bibliographic record
Abstract
Protein nutrition is important for human health because its deficiency leads to major public health problems such as Protein Energy Malnutrition. Soybean an excellent and cheap source of high quality protein has been introduced into the Western Kenya and other developing countries for improved health and food security, but end use qualities may reduce its utilization. The main objective of this study was to evaluate the protein nutritional quality of four soybean varieties grown in Western Kenya. Protein quality was determined using male weanling albino rats for the indices of net protein retention, food efficiency, apparent and true protein digestibilities and faecal weight. Soybean amino acid efficiency was evaluated using Protein digestibility corrected amino acid score (PDCAAS). Soybean diet SB 132 had the highest protein nutritional quality with the best protein retention of 6.29 g, Apparent Protein digestibility of 89.13%, True Protein Digestibility of 96.48%, weight gain of 5.50 g and a Net Protein Retention Ratio of 4.70. All the four soybean varieties had high amino acid profiles with a Protein digestibility Corrected Amino Acid Score of 1.0. Soybean variety SB 132 is the most superior in digestibility and the study recommends its promotion as a food crop in Western Kenya and other developing countries for the management of Protein Energy Malnutrition and for food security.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".