Organoleptic Analysis and Nutritional Content of Biscuits Based on Purple Sweet Potato and Seaweed Flours
Bibliographic record
Abstract
Development of the right formula for making biscuits based on purple sweet potato flour and seaweed flour can be used as supplementary feeding for Toddlers to meet nutritional needs and prevent malnutrition in toddlers.The research objective was to analyze biscuits' nutritional and organoleptic content based on purple sweet potato flour and seaweed flour.The research method was experiment and laboratory test.The main ingredients of the study were purple sweet potato flour and seaweed.The research variable was sensory quality (organoleptic test): taste, texture, aroma, and color; for sensory testing, all treatments would be presented to panelists, and panelists determined the most preferred one.The sample from the sensory test with the highest score (the most preferred by the panelists) was then analyzed for its chemical quality.Chemical quality included water, protein, fat, ash, carbohydrate, and total calories of biscuits.The results showed that the best formula for biscuits made from purple sweet potato flour and seaweed flour was formula E (100% purple sweet potato flour and 0% seaweed flour), with the panelists' preference for the taste of 4.2, the texture of 3.5, aroma of 4.3, and color of 4.2, out of 5.The average nutritional content obtained in biscuits already met the requirements of the Indonesia National Standard, including water content, ash content, protein content, fat content, and carbohydrate content.The highest carbohydrate, protein, and water content were detected in formula E, 70.35%, 9.70%, and 4.14%, respectively; the highest fat content was in formula B, about 16.38%.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".