Extraction of resinoids from St. John's wort (Hypericum perforatum L): I. Efficiency and optimization of extraction
Bibliographic record
Abstract
The extraction of resinoids from St. John's wort (Hypericum perforatum L) was studied in a series of two papers. In the first part, the effects of the operating conditions on the yield of resinoids (total extract) were analyzed, while the mathematical models of extraction kinetics were compared in the second one. The extraction was carried out using an aqueous solution of ethanol (70 and 95 % v/v) at a hydromodulus (plant material to solvent ratio, w/v) of 1:5 or 1:10. The plant material was disintegrated and divided into three fractions (mean particle size: 0.23, 0.57 and 1.05 mm). The temperature was 25, 50 or about 80?C (boiling temperature). A higher yield of resinoids was obtained when the plant material of greater disintegration degree (0.23 mm) was treated with 70% v/v aqueous ethanol solution at higher hydromoduli (1:10) and temperatures (80?C). The effects of the operating factors on the yield of resinoids were estimated by using both the full factorial experimental plan 24 and artificial neuronic networks (ANN) of 3-4-1 topology. Of the two methods, the ANN one was found to be advantageous because of its capability of estimating the yield of resinoids in the whole range of the applied operating conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".