Experimental and modeling studies on extraction of catechin hydrate and epicatechin from Indian green tea leaves
Bibliographic record
Abstract
Abstract A systematic investigation of effect of various parameters, for example, temperature, speed of agitation, particle size, and solid loading, on the percentage extraction of catechin hydrate (CH) and epicatechin (EP) was conducted. The extraction was performed with water, methanol, and ethanol. Water was found to be the best solvent for both. Percentage extraction of CH was found to decrease with temperature beyond 46°C; however, this is not the case for epicatechin. The thermal stability analysis of both the compounds was performed to ensure decomposition. This supported the experimental observation of batch extraction. Size of particle has little effect on percentage extraction of EP, but it increases for CH with decrease in particle size, which is probably because of decrease in diffusion path length. Assuming flat geometry of the particles, the process is modelled and compared with experimental data at different experimental conditions. The experimental data fitted well with the model proposed by Wongkittipong. The effective diffusion coefficients through the solid matrix of Indian green tea leaves for CH and EP estimated from the diffusion model were found to be in the range 1.29 × 10 –13 to 3.40 × 10 –13 m 2 /s and 1.20 × 10 –13 m 2 /s to 3.38 × 10 –13 m 2 /s, respectively. The effect of temperature on diffusion coefficient of CH and EP was determined using the model. The energy of activation required for diffusion was found to be 32.78 and 30.28 kJ/kmol, respectively for CH and EP.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".