Nutritive Assessment of Composite Flour Biscuit Incorporated with Herbal Plant Powder
Bibliographic record
Abstract
A cookie is a baked product, which can be prepared by using wheat flour with non-wheat materials enriched with nutritional value. The study aims to formulate herbal cookies incorporating Asparagusracemosus and Aervalanata stem and leaf powder enriched with mungbean (Vignaradiata) and wheat flour. Each of the herbal plant powder and flour mixture was formulated at a ratio of 1:1 by drying and grinding into a fine powder. Three different cookie formulas by varying combinations of plant powder and flour mixture were evaluated based onthe 5-point hedonic scale on sensory attributes by a 30 member untrained panel.According to the results of sensory evaluation, the sample enriched with 4 % plant powder and 55 % flour mixture was accepted.The phytochemical analysis indicated the presence of secondary metabolites including Alkaloids, Saponins, Flavonoids, Tannins, Phenols, Terpenoids, Glycosides and Steroid in the accepted cookie sample. The cookies were rich in carbohydrate (59.3%), fat (14.3%), protein (13.4%) and the calculated energy value was 419.5 kcal. Peroxide value and acid value of stored cookies were lower than the maximum recommended level. The IC50 value for the methanol extract of the herbal cookies was 16.69±0.88 mg/ml.The bacterial count of cookies was 6.0 ×102CFU/g after 6 weeks of storage and this was well below the safe level of 1×104 CFU/g. There was no yeast and mold growth observed during the storage. Overall analysis proved that herbal cookies were acceptable for human consumption after storing it under room temperature within six weeks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".