Exploring Home-use Test to Assess Urban Consumers’ Acceptance and Likelihood to Purchase Naturally Fortified Instant Whole Meal Sorghum-maize Flour Blends in Eldoret, Kenya
Bibliographic record
Abstract
Fortification of staple foods has the potential to alleviate micronutrient and protein energy malnutrition in sub Saharan Africa. However, natural food fortification often alters sensory attributes such as flavour, aroma, appearance, texture and other features in ways that may affect target consumer overall acceptance and willingness to purchase. This study examined urban consumers’ acceptance and likelihood to purchase wholemeal instant flours that were fortified using plant based sources. A home-use test (HUT) sensory experiment was conducted in Eldoret, Kenya among 154 urban dwellers in the middle and high level income group living in three gated estates. 5 different flour composites using sorghum, maize, baobab, orange fleshed sweet potato (OFSP) and grain amaranth were used to make both thin (uji) and thick (ugali) porridges. The results showed that urban consumers could distinguish stiff porridge (ugali) and thin porridge (uji) made from the 5 flour varieties. They preferred uji, expressed by higher mean general acceptability scores made from all the flour varieties (M=4.15-M=3.83) to ugali (M=3.50-M=3.17), for appearance, aroma, texture in hand and mouth, significant at p < 0.05. Mothers’ and childrens’ overall acceptance ratings for both sets of products did not differ, showing the ability of mothers to influence a child’s overall acceptance of a product. Further, more than 80% consumers were likely to purchase and use the instant flour. Pearson correlation showed significant positive correlations (*P<.05; & **P<.01), for product fit for all family, with nutritional and health benefits, and product that is introduced by a close friend as the main factors driving their likelihood of purchase. We conclude that HUT is effective for assessing consumer acceptance as far as product sensory characteristics and consumer adoption of a new product, and can be used by industry before market penetration.
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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 | 0.001 | 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) | 0.003 | 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".