Determination of Ethanol Content in Kombucha Products by Gas Chromatography with Flame Ionization Detection: A Multilaboratory Study
Bibliographic record
Abstract
Abstract Background: Kombucha is a nonalcoholic, fermented tea beverage that has recently received negative attention because of documented concentrations of ethanol in excess of allowable limits of ≥0.5% alcohol by volume (ABV). Objective: Our previously reported headspace GC with flame-ionization detection (GC-FID) method was adopted by the AOAC Expert Review Panel as First Action Official MethodSM 2016.12 in September 2016 based on published single-laboratory validation study results. This paper describes the corresponding multilaboratory study using this method to further validate its performance parameters. Methods: Four laboratories participated in the study and received practice samples, test samples, reference standards, and detailed protocols. Eight kombucha samples sent out to laboratories were randomly assigned sample numbers and were blinded in terms of content and identity. Each laboratory analyzed all samples using the GC-FID method and reported their results. Results: Cochran’s C-test and single and double Grubbs’ tests were used to identify and remove outliers. Horwitz ratio values for all samples were between 0.5 and 1.7. As per the Standard Method Performance Requirements (SMPRs®), all samples within the analytical range of 0.1–2.0 ABV% had RSDR values <6%. Conclusions: The results from this study demonstrate the evaluated GC-FID method meets the SMPR requirements and is fit for purpose for detecting ethanol in kombucha products. Highlights: Kombucha, a nonalcoholic, fermented beverage, has been found to contain ≥0.5% ABV. First Action Official Method 2016.12, a headspace GC-FID method for determining ethanol in kombucha, is supported by a multilaboratory study.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".