Optimization and Modeling of Energy Bars Based Formulations by Simplex Lattice Mixture Design
Bibliographic record
Abstract
Simplex lattice mixture design was utilized to optimize high caloric and acceptable energy bars. Fourteen formulations of injera were produced from flour blends of high quality cassava flour (0–100%), toasted bambara groundnut (0–100%) and roasted cashew kernel(0–100%).The study was carried out to evaluate the effect of varying the proportions of the independent variables on these dependent variables (proteins, fats, carbohydrate) and general acceptability qualities of the energy bars. Proteins, Fats and Carbohydrates were indicators of the calorie values of these energy bars. Mixture response surface methodology was used to model the proteins, fats, carbohydrates and general acceptability with single, binary and ternary combinations of high quality cassava flour, toasted bambara groundnut and roasted cashew kernel flours. The effect of variation in levels of cassava, bambara groundnut and cashew kernel flours on the fats, proteins, carbohydrates and general acceptability of the formulated energy bars were adequately predicted with regression equation. The statistical adequacy of the generated polynomial equation of the responses was checked using the following indices: F-value at p0.05, coefficient of determination R2, Adj. R2, lack of fit, and coefficient of variation (CV). Optimization suggested energy bars containing 61.40 % high quality cassava flour, 0.00 % bambara groundnut flour and 38.6 % cashew kernel flour as the best proportion of these components with a desirability of 0.775. Numerical optimization indicated that better sensory and high calorific qualities are directly related with the proportion of cassava flour, bambara groundnut flour and cashew kernel flour respectively. The optimum blends as validated showed a close relationship between the predicted and experimental values.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".