Consumer acceptance of foods derived from blended wheat flour in Nairobi, Kenya
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
• Consumers perceived wheat-based foods derived from blended flour as healthy. • Most consumers accepted chapati made from wheat flour blended upto the 10% level. • Bread derived from blended wheat flour blended above the 5% level was less preferred. • Information on flour composition increased preferences for foods derived from blended wheat flour. • Consumers prefer products derived from blended flour due to perceived health benefits. Governments across Africa have shown enthusiasm for wheat flour blending to reduce food security risks and pull demand for traditional but underutilized crops. However, research has sidestepped the question of whether consumers will accept foods derived from blended wheat flour. We used sensory evaluation and contingent valuation techniques with a sample of 1871 consumers in Nairobi, Kenya to measure the acceptance of two commonly consumed foods (chapati and bread) made from wheat flours blended with up to 20% sorghum, millet, or cassava flour. In blind tasting, bread made of blended flour was slightly less preferred than conventional bread, while chapati products made of wheat and sorghum (10%) or millet (5%) blends were equally valued as chapati made of 100% wheat flour, suggesting the potential to replace up to 10% of wheat flour in chapati without compromising sensory characteristics and consumer acceptance. When informed about the flour composition before tasting, consumers showed a stronger preference for the products made from blended flour and expressed a higher willingness to pay for blend-based products than conventional products. We discuss the policy implications of how consumer interest in such foods can be harnessed to advance food security and economic development goals.
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 | 0.000 | 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 | 0.000 |
| 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 it