Effect of different cereal blends on the quality of Injera a staple food in the highlands of Ethiopia
Bibliographic record
Abstract
Majority of the Ethiopian population are dependent on tef (Eragrostis tef (Zucc) trotter) flour to make injera, a staple food in Ethiopia, although injera could be made from different cereals. The price of tef, however, is high and the yield potential of the crop is low. Thus, searching for alternative cheaper grains and developing a blend of different cereal flours that can produce injera of acceptable quality and improved nutritional value would be important. This study was conducted to evaluate the sensory quality of injera made from a blend of different cereals (Tef, barley, sorghum and maize) with differing ratios: 100, 75, 50 and 25%. The sensory evaluation of injera was conducted at Mekelle University in a replicated trial. The results revealed significant differences among the cereal flour blends in injera texture, mouth feeling, suppleness and overall rate, while colour, taste and the appearance of injera surface gas holes were non- significant. Injera made from 100% tef flour got the highest preference rank in terms of the texture, mouth feeling, suppleness and overall ratings. Injera made from 50:50 tef + barley blend was the second best in both texture and suppleness followed by 50:50 tef + sorghum, 50: 50 tef + maize blends and tef + barley + sorghum blend of equal ratio. Similarly, results from blend of tef + barley + maize, tef + sorgum + maize and from the four varietal blends in equal ratios produced very good injera quality. From the study results injera quality ranked next to sole tef (tef + barley, tef + sorghum, tef + maize in 50:50 blends and tef + barley + sorghum in equal ratios) could be used as an alternative option for injera utilization and could provide nutritional and dietary benefits to consumers.Keywords: Sensory attributes, Injera quality, cereal flour blends, Tef, Ethiopia.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".