Sensory and nutritional evaluation of nine types of millet substituted for polished white rice in select Indian meal preparations
Bibliographic record
Abstract
This study was conducted to test the suitability of using nine types of millets namely finger millet, pearl millet, white and yellow sorghum, little millet, barnyard millet, proso millet, kodo millet, and browntop millet in seven popular Indian meal preparations based on sensory characteristics and nutrient value. The popular Indian meal preparations tested were boiled grain, dosa, idli, bisi belle bath, pulao, puttu, and pongal. In total, 53 variations in meal preparations were developed using the millets and seven polished white rice-based meal preparations were developed as control. The main findings indicated that meal preparation crafted from various millets garnered overall sensory scores closely resembling to those derived from polished white rice. Notably, little millet exhibited high scores in pongal and dosa, and achieved elevated overall sensory scores compared to meal preparation from polished white rice. Bisi belle bath made of barnyard millet scored higher in overall sensory score than polished white rice. Moreover, there was significant association between some types of millets’ overall sensory characteristics ( p < 0.005) with polished white rice-based meal preparations. In terms of nutrient value, all the millet-based meal preparations had significantly high nutritional value compared to those made with polished white rice ( p < 0.05). Especially calcium content of the meal prepared with finger millet was significantly higher compared to polished white rice-based meals ( p < 0.05). Puttu, idli, and dosa prepared with finger millet had calcium content of 59.4, 10.8, and 70.9 mg/100 g compared to those prepared with the polished white rice which had only 1.3, 6.3, and 9.2 mg/100 g. The magnesium content of all millet-based meal preparations was generally several-folds higher compared to the polished white rice-based meal preparations ( p < 0.05). There is a significant difference in the fiber content of the meals prepared with millets compared to the meals prepared with polished white rice ( p < 0.05). This study was conducted using millets that are locally available and does not represent all the millet varieties available globally, as each type of millet has a wide range of varieties. Therefore, it is important to understand and choose the type and variety of millet while enhancing the nutritional value of diets.
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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 | 0.001 | 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 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".