Effect of soybean (<i>Glycine max</i>(L.) Merr.) flour inclusion on the nutritional properties and consumer preference of fritters for improved household nutrition
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 ofPEM. 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 alleviatePEMamong fritters consuming populations of developing countries, particularly in Sub‐Saharan Africa.
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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 | 0.001 |
| 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) | 0.002 | 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".