Nutritional quality and sensory acceptability of complementary food blended from maize (<i>Zea mays</i>), roasted pea (<i>Pisum sativum</i>), and malted barley (<i>Hordium vulgare</i>)
Bibliographic record
Abstract
Abstract The aim of this study was to evaluate the effect of blending ratio of malted barley, maize, and roasted pea flour on complementary food quality and sensory acceptability. D‐ Optimal mixture design was used to generate 14 formulations. Each ingredient had 55–90% maize, 20–35% pea and 4–12% malted barley. Pretreatments like debranning of maize, roasting of pea and dehusking of malted barley were done. The three component‐constrained mixture design was conducted using Design‐Expert® 6 (Stat‐Ease). Ash, protein, fat, fiber, moisture, and carbohydrate contents were found in between range of 1.5–2.5%, 13.0–18.5%, 1.8–2.5%, 3.06–4.45%, 5.0–6.5%, and 68.9–74.1%, respectively. Significant difference (P < 0.05) among the treatments was observed for protein, moisture, odor, flavor and sensory overall acceptability. Lack‐of‐fit was significantly different only for fat (R2 = 0.90). Thus, the model generated can predict all attributes except for fat. The optimum values of high nutrient content and sensory acceptability were observed in the range of 55.0–68.5%, 27.5–35.0%, and 4.0–10.0% for maize, pea, and malted barley respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".