Prevalance And Amounts Of B-Vitamins And Taurine In Popular Energy Drinks In Indian Market
Bibliographic record
Abstract
Energy drinks are among the most popular packaged beverages consumed globally, falling under the category of functional beverages, which also includes sports drinks and nutraceutical beverages. They contain a variety of ingredients which, as claimed by the manufacturers, has a beneficial impact on its consumer’s health. This study focused on examining the nutrition fact information on the product labels of the top-selling energy drinks in India to analyze B-vitamins and taurine profiles. The results were compared with the ingredient profiles listed on the product labels and the standards set by FSSAI for energy drinks. The top 10 commercially available energy drinks in the Indian market were identified through a multiple commercial retail websites. 10 samples each of 10 different energy drink brands popular in Indian market were included in this analysis, which makes a sample size of 100 samples. The ingredients selected for this study were as follows: vitamin B2, vitamin B3, vitamin B6, vitamin B9, and taurine. The findings of this study suggest a high prevalence of B-vitamins and taurine in all these energy drink brands, with many of the formulations containing well above the recommended values by FSSAI and below the concentrations mentioned on the product label, suggesting significant discrepancies and non-compliance with FSSAI standards as well as the product lables.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".