Nutritional quality and acceptability of sweet potato–soybean–moringa composite porridge
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
Purpose – The purpose of this paper was to formulate porridge using orange-fleshed sweet potato (OFSP), soybean and moringa ingredients that optimizes its nutritional quality and acceptability. Design/methodology/approach – A 16-run constrained D-optimal mixture design was used to evaluate proximate compositions and sensory acceptability of the products. Each composition and acceptability response variable was optimized separately, and then, the sweet spot that optimizes all was determined. Findings – The protein, fiber, total ash, carbohydrate, iron and carotenoid contents as well as major sensory quality indicators were significantly affected by soybean, moringa and OFSP blends. However, the influence of the mixture on fat content was weak. Sensory acceptability was high for porridges processed from high OFSP and soybean, but higher nutritional quality was obtained from higher moringa levels. Graphical optimization showed that blends containing 68-75 per cent OFSP, 17-26 per cent soybean and 5-8 per cent moringa have produced nutrient enriched porridges with desirable sensory quality. Originality/value – The study showed that OFSP, soybean and moringa have a potential for making protein, carbohydrate, dietary fiber, pro-vitamin A carotenoids and iron enriched product that will contribute to the fight against malnutrition in developing nations such as Ethiopia. In addition, having OFSP in the blend masks undesirable odor and taste imparted by moringa.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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