Application of Check-All-That-Apply (CATA) for formulating and characterizing sargassum seaweed-based kombucha drink: Effects of different sugar types and fermentation times
Bibliographic record
Abstract
One approach to refining the taste of Sargassum tea involves subjecting it to fermentation to produce Kombucha tea. This study was designed to assess the impact of fermentation duration and various types of sugars on the sensory attributes of Seaweed Kombucha, alongside the physicochemical properties and flavor profile associated with the optimal sugar type and a fermentation duration. Seaweed Kombucha samples were subjected to treatments involving different sugars (sucrose, sorbitol, and steviol) and fermentation duration of 9 days, with sampling intervals at days 0, 3, 5, 7, and 9. The characteristics of Seaweed Kombucha were evaluated based on parameters such as pH, alcohol content, color, total sugar content, and sensory analysis. Utilizing hedonic sensory assessment, the most favorable Seaweed Kombucha sample was identified as the one treated with steviol sugar and fermented for 3 days. The sensory flavor profile of Seaweed Kombucha was elucidated through Check-All-That-Apply (CATA) and Gas Chromatography-Mass Spectrometry (GC-MS) methodologies. The optimal Seaweed Kombucha sample exhibited a volatile compound profile comprising acids, alcohols, ketones, heterocyclic compounds, and other constituents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".