Remote Organoleptic Testing System Using Pairwise Comparison Scale in Sensory Evaluation of Food Products
Bibliographic record
Abstract
This research aims to develop and evaluate a remote organoleptic testing system using a paired comparison scale.This research follows the Software Development Life Cycle (SDLC), which includes planning, analysis, design, implementation, and maintenance.The system was tested with 7 respondents: 5 sensory experts in the field of food science and 2 web developers.The main aspects assessed for system feasibility included system accessibility, handling of scaled questionnaires, questionnaire delivery and storage, user interface, functionality, performance, and user satisfaction.Developed using PHP, JavaScript, and MySQL, the system allows panellists to conduct remote evaluations from various locations.The evaluation results showed a total score of 457 out of 525, resulting in a feasibility rating of 87.0%.These findings indicate that the system is highly feasible for practical implementation, providing a robust solution for remote organoleptic testing.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".