Consumer acceptance of quality protein maize (<scp>QPM</scp>) in East Africa
Bibliographic record
Abstract
BACKGROUND: Undernutrition in sub-Saharan Africa remains problematic, and quality protein maize (QPM) can benefit populations whose diets are heavily based on maize and who are consequently at risk for inadequate intakes of quality protein. However, changes in the chemical composition of QPM may affect its sensory characteristics and, hence, acceptance. Acceptance tests were therefore conducted to evaluate QPM varieties in three East African countries using central location tests with one or two varieties in each country, using the most popular preparations: ugali (Tanzania), githeri (Kenya) and injera (Ethiopia). In total, 281 urban and rural consumers of both sexes and varying levels of education evaluated the products on standard sensory criteria: appearance, aroma, texture, taste and overall, using a Likert scale. RESULTS: The results show that African consumers can differentiate QPM products from their conventional counterparts, indicating that the QPM trait results in distinguishable sensory changes. Analysis by ordinal mixed regression models showed that consumers found QPM acceptable and even preferable to conventional maize. CONCLUSION: The sensory characteristics of QPM are therefore no impediment to its adoption; on the contrary, when coupled with good agronomic performance, they may help its utilization, leading to a positive impact in nutritionally vulnerable populations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".