Optimization of Beverage Formulation with Technique for Order of Preference by Similarity to Ideal Solution
Bibliographic record
Abstract
This paper presents a new optimization approach for beverage formulation using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The comparison and ranking of candidates would be the essential steps for the evaluation of beverage production, which in turn requires the quality control of beverage ingredients. The quality of the beverage depends on the type and amount of the ingredients used for their preparation. This emphasizes the importance of the optimization of beverage formulation, which is addressed in this paper using the TOPSIS method. The results of this investigation show that ingredients with more impact on human health (having bigger weights) could affect the rank of drinks. The second, fourth, third and first candidates ranked first, second, third and fourth before and after the changes of criterion types and weight values. Moreover, the change of the criterion type accompanied with the increase in its concentration in the beverage had a significant impact on the candidates’ ranks. The similarity coefficient of the fourth candidate for which this ingredient concentration changed showed a decrease from 0.625 to 0.500. The optimization of beverage formulation with the modified TOPSIS algorithm showed the efficiency of the automated decision-making process with the obtainment of the same ranks in comparison with those of the analyses without the consideration of the candidates’ colors as they were used in beverages and could not determine their quality. The results in this research paper could be used for the quality improvement of drinks in the beverage industry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".