A Simple Approach To Detect Caffeine in Tea Beverages
Bibliographic record
Abstract
The photophysical properties of commercially available dye Acridine Orange (AO) describe an excellent probe for selective and sensitive detection of caffeine in aqueous solution. AO exists in monomer-dimer equilibrium in water. AO monomer is a fluorophore, but the dimer is not. Addition of caffeine to the AO leads to a shift in the monomer-dimer equilibrium toward the direction of AO monomer (fluorophore) and results in enhancement of AO fluorescence intensity. Enhancement of AO fluorescence intensity in the presence of caffeine has been treated as a signal for caffeine sensor. Furthermore, the caffeine-induced shift in AO monomer-dimer equilibrium is attributed to the binding of caffeine with AO monomer, and the binding constant was higher at a low pH range (pH ~2) compared to pH ~7, which results in superior caffeine sensitivity at pH ~2. Finally, caffeine content in commercial tea beverages has been evaluated and compared with the value obtained with a standard HPLC method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".