Determination of Ethanol Content in Kombucha Using Headspace Gas Chromatography with Mass Spectrometry Detection: Single-Laboratory Validation
Bibliographic record
Abstract
BACKGROUND: Kombucha is a fermented beverage made with tea, sugar, and a symbiotic colony of bacteria and yeast that is usually marketed as a non-alcoholic beverage. Products must contain <0.5% and <1.1% alcohol by volume in the United States and Canada respectively to be classified as non-alcoholic products. Prior studies have found that Kombucha beverages can become very acidic and may contain levels of alcohol above 1% which can be a potential health risk to children and the developing fetus during pregnancy. OBJECTIVE: Given the public safety concerns and legal requirements associated with the level of alcohol within Kombucha beverages, there is a need for accurate and reliable methods. Herein we describe the validation of a sensitive, rapid, and simple Headspace Gas Chromatographic method with mass spectrometric detection for determining ethanol in Kombucha. METHODS: Method performance characteristics measured included linearity, accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ) as per AOAC International guideline Appendix K Part 1. Performance was evaluated against the AOAC Standard Method Performance Requirements 2016.001 for determination of ethanol in Kombucha. RESULTS: The linear dynamic range for this method was confirmed over the range of 0.025 to 2.47% ABV. The LOD and LOQ were determined to be 0.0002% and 0.002% ABV, respectively. With a spike recovery of 102% for accuracy and precision of RSDr ≤ 4% the method met the SMPR requirements within the analytical range. CONCLUSIONS: The results of this validation study demonstrated the method is fit for the purpose of quantifying ethanol in Kombucha and is suitable for rapid and easy integration by laboratories to ensure that regulatory requirements are met.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".