Green Tea Product Epigallocatechin Gallate (EGCG) Content and Label Information: A Descriptive Analysis
Bibliographic record
Abstract
Green tea’s popularity can be largely attributed to its potential health benefits, with an emphasis on antioxidant properties from its catechin constituents, especially (−)-epigallocatechin-3-gallate (EGCG). EGCG appears to be responsible for many of the potential health benefits of green tea. However, while higher intake levels may provide benefit, lower intake levels may not. The objective was to determine whether commercially available green tea products provide label information about EGCG content and other constituents and then to analyze the label information in terms of existing research. A descriptive analysis of product label information was conducted. In total, 105 green tea products evaluated, 58% of green tea supplements and 5% of green tea beverages included information about EGCG content on the label. Among the dietary supplement products providing sufficient information on the label, the amount of EGCG listed ranged from 70 mg to 600 mg per serving. The average EGCG per serving was 223.7 mg. The average reported caffeine content was 56.0 mg per serving. In conclusion, most green tea beverages to not provide adequate information about EGCG or other constituents. Green tea supplements are more likely to provide this information. One to two servings of green tea supplements are typically needed to achieve EGCG or catechin intake levels similar to those demonstrating efficacy in clinical studies. Consumers should consider selecting products that adequately describe constituent information on the label. Manufacturers should consider providing this essential information on the product label in order to better inform consumer decision-making.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".