Identifikasi kandungan kafein dalam ekstrak etanolik daun teh (Camellia sinensis L.) dari daerah Boyolali dengan metode KLT-Densitometri
Bibliographic record
Abstract
One of tea garden in Jawa Tengah is located at Kabupaten Boyolali, the exact place is Kecamatan Selo which is located at 1.300-1.500m above the surface of the sea.A higher area will influence tthe amount and effect of chemical contains in a plant.One of tea beverage which is mostly consumed by Indonesian people is green tea, which is used as a stimulant because of it's caffeine contains.Caffeine in tea's leaves in base forms can be extracted by ethanol 70% because caffeine in base forms is soluble in organic solvent.According to the chromophor in caffeine structure which is responsible to absorption of UV radiation energi, TLC-densitometry method is used as quantitative identification of caffeine contains.The purpose of this research is to determine the amount of caffeine in tea's leaves ethanolic extract.This research is a nonexperimental research because there is no treatment to the subject.The step of this research are consist of qualitative identification, a determination of caffeine by TLC, and then followed by quantitative identification by TLC-densitometry.The principle of this method is by measuring the density of sample chromatogram which is separated by TLC and compared to the density of standard chromatogram wich is eluted together.The result of qualitative identification by TLC shows that tea's leaves ethanolic extract contains caffeine with Rf 0,39 compared to caffeine standard with Rf 0,40.While quantitative identification by densitometry shows that the rate amount of caffeine in tea's leaves ethanolic extract from Boyolali is (1,2439 0,1039) % w / w .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.243 | 0.002 |
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; both teacher heads agree on what is shown here.
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".