Effect of Caffeine Adsorption by Activated Charcoal in Green Coffee Extract on Blood Glucose Reduction in Rats
Bibliographic record
Abstract
<p><strong>ABSTRACT.</strong> Green coffee beans (<em>Coffea Canephora</em>) contain chlorogenic acid, an active compound known to aid in lowering blood glucose levels. However, the caffeine content in green coffee beans can reduce insulin sensitivity, thereby diminishing glucose tolerance. This study aims to improve the hypoglycemic potential of green coffee by removing caffeine through adsorption using activated carbon. Green coffee beans were extracted in water at 60–70°C, and caffeine adsorption was carried out with activated carbon. FTIR analysis was performed on the activated carbon to confirm caffeine adsorption, while HPLC analysis was conducted on the green coffee extract before and after adsorption to determine caffeine and chlorogenic acid contents. Hypoglycemic activity was evaluated in alloxan-induced albino Wistar rats (150–250 g). Statistical analysis using the t-test was employed to assess the effectiveness of caffeine adsorption in lowering blood glucose levels. Activated carbon reduced caffeine content by 23.71%, as confirmed by FTIR spectra showing hydrogen bonding interactions between the –OH groups of activated carbon and the C=O or N atoms of caffeine. Rats receiving the caffeine-reduced green coffee extract reached normal blood glucose levels faster than untreated diabetic rats. The effectiveness of caffeine adsorption was supported by the t-test results, which showed a significant reduction in mean blood glucose levels compared with the untreated diabetic group (p = 0.002) and from baseline values (p = 0.004). Although the treated diabetic group still differed significantly from the normal control (p = 0.021), the glucose level approached the normal range, indicating substantial recovery toward normoglycemia.</p><p><strong>Keywords:</strong></p><p>Green coffee beans, Caffeine, Chlorogenic acid, and Blood glucose</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".