Sensory Acceptability of Multiple-Micronutrient-Fortified Lentils in Bangladesh
Bibliographic record
Abstract
In this study, panelists in rural Bangladesh (n = 150) assessed the sensory attributes of two cooked and uncooked dehulled red lentils: the control (unfortified lentils) and lentils fortified with eight vitamins and two minerals (multiple micronutrient fortified; MMF). The panelists evaluated the appearance, odor, and overall acceptability using a nine-point hedonic scale (1 = extremely dislike; 9 = extremely like). The taste and texture of the cooked lentils, prepared as South Asian lentil meals, were assessed. Consumer responses varied significantly in the appearance of the uncooked lentils but were similar in odor and overall acceptability. Meanwhile, the five traits of the cooked lentils, including overall acceptability, showed significantly similar consumer responses. This suggests that fortification had a minimal impact on the sensory qualities of the MMF lentils. Furthermore, a highly significant (p < 0.0001) correlation coefficient (with values ranging from −0.98 to 0.97) was observed between HunterLab colorimetric measurements (L = luminosity, a* = red hue, and b* = yellow hue) and sensory trait ratings. The Cronbach’s alpha (CA) score for both the cooked control and MMF lentils was 0.79. The average CA score for the cooked lentils was 0.79, while for the uncooked lentils, it was 0.71, demonstrating the strong reliability of the panelists’ assessments. Overall, the sensory qualities of the MMF lentils were acceptable and did not differ significantly from those of the control lentils.
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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 | 0.001 |
| 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 | 0.001 | 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".