Effect of soybean (<i>Glycine max</i>(L.) Merr.) flour inclusion on the nutritional properties and consumer preference of fritters for improved household nutrition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Diets in populations of most developing countries are often deficient in protein, carbohydrates, and fat, leading to protein‐energy malnutrition ( PEM ). Diet‐based strategies are the most promising approach for a sustainable control of PEM . This study aimed to investigate the effects of soy flour inclusion on the nutritional properties, consumer preference, purchase intent, and willingness to pay for wheat‐based fritters. The proximate composition of both types of fritters was determined using standard methods, Consumer preference survey on organoleptic properties was carried out among 291 participants (93 men, 198 women) in Chipata, Katete, and Lundazi districts of Eastern Zambia. The soy‐fortified fritters had significantly higher ( p < 0.05) levels of ash, fat, amylose, crude fiber, and protein than the unfortified fritters. Protein, crude fiber, amylose, and ash contents of soy‐fortified fritters were considerably increased by 55.5%, 18.9%, 98%, and 30.6%, respectively. The overall preference showed no significant difference ( p > 0.05) between unfortified and soy‐fortified fritters. A larger percentage of participants in Katete (38%) and Chipata (41%) preferred the soy‐fortified fritters to the nonfortified one. In addition, no significant difference ( p > 0.05) was also observed for intention to purchase between both types of fritters across the three locations. In conclusion, incorporating 20% soybean flour into fritters, which showed better nutrients quality, could be used to alleviate PEM among fritters consuming populations of developing countries, particularly in Sub‐Saharan Africa.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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