Nutritional Formulation and Analysis, and Organoleptic Tests of Functional Chocolate Biscuits “Morisoya” –Supplementation of Moringa Leaves (Moringa oleifera L.) AND Soybeans (Glycine max (L.) Merill)
Bibliographic record
Abstract
Moringa (Moringa oleifera) and soybeans (Glycine max) are the kinds of crops that have been known widely to the public. They both can potentially be a source of additional nutritional supplements in a variety of processed snacks, such as biscuits. This study aimed to determine the ratio of formulas in making biscuits from moringa leaf extracts and soybean extracts and the nutritional content of biscuits “Morisoya”, and to conduct an organoleptic test with supplementation of moringa leaf extracts and soybean extracts. It employed a completely randomized design (CRD), consisting of nine treatments and two replications. The variables observed included: (a) protein content of chocolate biscuits “Morisoya”, (b) carbohydrate content of chocolate biscuits “Morisoya”, (c) fat content of chocolate biscuits “Morisoya”, and (d) organoleptic/hedonic test on biscuits “Morisoya”. The results show that the highest protein content is found in the treatment P8 (32.50%), but it is not significantly different from that found in treatments P9 (31.68%) and P7 (29.67%), followed by the treatment P6 (28.61%), which are not significantly different from that in the treatment P5 (26.46%). The highest carbohydrate content is found in the treatment P1 (62.70%) while the highest fat content is found in the treatment P9 (28.92%). The highest result for the organoleptic/hedonic test (on a scale of 0 to 5) is generated by the treatment P6 (3.28), with the highest percentage of 13% compared to the control treatment and other treatments. Based on results of the analysis on protein, carbohydrate, and fat contents as well as results of the organoleptic test, it is apparent that the treatment P6 constitutes the formula for biscuits “Morisoya” which was then selected to be developed in further research as functional biscuits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".