Optimization and Marketing Analysis of Low-Oxalate Beneng Taro Flour (Xanthosoma undipes K. Koch) in Gluten-Free Noodles
Bibliographic record
Abstract
The purpose of this study was to analyze the optimization and marketing analysis of Low Oxalate Taro Bread Flour (Xanthosoma undipes K. Koch) in Gluten Free Noodles.This experimental research was conducted from April to May 2022 at the Laboratory of the Indonesian Center for Agricultural Postharvest Research and Development, Bogor.Making gluten-free Taro beneng noodles was then analyzed for physical and chemical properties as well as sensory analysis.Then continued to the marketing analysis stage of gluten-free taro noodles using a WEB-based GIS approach.The research findings revealed that both Formula A and Formula B of taro beneng noodles met the moisture content requirements for dry noodles according to SNI 8217-2015.Formula A had higher ash and protein content, while Formula B had higher carbohydrate and dietary fiber content.Additionally, Formula A exhibited higher levels of oxalic acid and calcium oxalate compared to Formula B.Moreover, Formula B demonstrated lower cooking loss and shorter cooking time than Formula A. The hedonic test results indicated that both Formula A and Formula B of beneng taro noodles were generally well-liked by the participants.The average ratings for color, aroma, texture, and overall preference fell within the range of "like" to "rather like".The research findings suggest that both Formula A and Formula B of taro beneng noodles were generally well-liked by the participants, with similar ratings for color, aroma, texture, and overall preference.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".