Semi-Automated Enzymatic Determination of Ethanol in Beverages: Collaborative Study for RIDA®CUBE Ethanol
Bibliographic record
Abstract
Easy and quick methods to quantify ethanol reliably in beverages are always important. In 2022, the EnzytecTM Liquid Ethanol test kit was approved as AOAC Official MethodSM 2017.07 Final Action after a collaborative study was conducted with different beverages such as kombucha, juices, and beer. During set-up of this collaborative test, small sized companies asked to include the RIDA®CUBE Ethanol/RIDA®CUBE SCAN device since it is easy to use, suitable for a few samples only and contains the identical reagents as the EnzytecTM Liquid system. It is applicable to quantify ethanol in diluted kombucha, fruit juices, and alcohol-free beer samples around 0.5% alcohol-by-volume within 12 min. The overall relative reproducibility standard deviation across a wide concentration range for kombucha, was calculated to be 6.29%. Analysis of juices and beer showed an overall higher variation with an estimated overall RSD(R) value by regression of 14.4%. The data obtained by this collaborative study show that the RIDA®CUBE Ethanol in combination with the RIDA®CUBE SCAN device is suitable to quantify ethanol from matrices representing important alcohol-free liquid food categories.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".