Assessment of Pliek-U Sensory Attributes: A Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method Application
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Bibliographic record
Abstract
Sensory assessment plays an important role in solving the problem of consumer preferences and acceptance of food products.This study aims to conduct a Pliek-U sensory assessment through a multi-criteria decision-making system using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method.MOORA is a multi-objective system that has a good level of selectivity in determining an alternative.There were 7 commercial Pliek-Us obtained from the local Aceh market (P1, P2, P3, P4, P5, P6 and P7).The sensory criteria assessment of commercial Pliek-U included color (C1), aroma (C2), taste (C3), texture (C4), aftertaste (C5), defects (C6), and overall acceptance (C7), which used a hedonic scale (1-7).These 7 sensory attributes are able to describe the expected quality of Pliek-U products and are often used as indicators to assess the sensory properties of food products in Indonesia.Sensory assessment was carried out by 50 panelists from Pliek U consumers.The findings of this study each commercial Pliek-U product used had its own characteristics affecting preference of the panelists.The Pliek-U product (P4) obtained the highest score with a value of 0.390 (rank 1).The characteristics are that it has a distinctive odor from Pliek-U, dark brown in color, having an acid taste favored by panelists, having a dry texture, no taste remains in the mouth, and no other taste which appeared when Pliek-U was eaten.Based on overall acceptance, this Pliek-U was highly preferred.Overall, the results of this study indicate that sensory assessment is very important to assess the attributes or quality of Pliek-U.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it