Analysis Of Caffeine And Artificial Sweeteners As Active Ingredients In Popular Energy Drinks Available In Indian Market For Forensic Prospects
Bibliographic record
Abstract
Energy drinks (EDs) are beverages designed to boost energy, alertness, and concentration. They typically contain caffeine, sugar, vitamins, and other ingredients like taurine, ginseng, and B vitamins, though exact ingredient amounts are often undisclosed. These drinks are popular among people looking for a quick energy boost, especially in situations requiring extended periods of wakefulness or physical activity. However, they can have side effects, particularly when consumed in large quantities or mixed with other drinks. They are typically marketed to enhance physical or cognitive performance and promote weight loss by increasing energy expenditure, owing to the presence of active ingredients of these drinks. This study aimed to measure the concentrations of such active ingredients of energy drinks using HPLC and based on the analysis result, assess whether the product label claims stand true or not and whether the product complies with FSSAI standards. Samples from ten different ED brands were analyzed using HPLC for determining levels of active ingredients of EDs, i.e., caffeine and artificial sweeteners and explore its scope in forensic science. The study found significant discrepancies and non-compliance with standards across all brands with high prevalence, well above the recommended values in all the samples, suggesting potential health risks and highlighting consumer fraud from a forensic perspective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".