Production Process of Canada Goldenrod Herb (Solidago canadensis) Tincture and Extract
Bibliographic record
Abstract
INTRODUCTION. Pre-treatment of local herbal substances is an essential growth vector in pharmaceutical industry. Canada goldenrod ( Solidago canadensis L.) is a promising source of anti-inflammatory and diuretic substance widespread in Russia and Belarus. Pre-treatment of herbal substances increases the yield of biologically active substances (in particular flavonoids) during extraction, a trait useful for obtaining tinctures and extracts of Canada goldenrod herbs. AIM. This study aimed to develop processing technology of tinctures and extracts obtained from pre-treated Canada goldenrod allowing to increase the content of flavonoids. MATERIALS AND METHODS. Canada goldenrod herb was the study object. Four pre-treatment options were studied: heat pre-treatment, defatting, and their combinations. The content of flavonoids was determined by high-performance liquid chromatography. Gas chromatography was used to define residual organic solvents. RESULTS. The highest yield of flavonoids in tinctures was observed with ethanol volume fraction of 60–70%, raw materials to extractant ratio 1 g to 25 ml, grinding degree of raw materials 2,000 μm, and a settling time of the primary extract no more than four days for remaceration. The highest content of flavonoids in dry extracts is achieved with 90% relative distillation volume, 80 °C distillation temperature, 40 min minimum distillation time, 6 cm thickness of the distilled layer, and no more than 4 days settling time of the primary extract. The highest yield of flavonoids in the tincture is observed in heat pre-treatment of Canada goldenrod herb and in pre-treatment defatting of the herbal raw material for the dry extract. CONCLUSIONS. Optimal technological parameters for production of Canada goldenrod herb tinctures and extracts have been established. The above technologies developed considering pre-treatment stage can be used to produce the specified extracts of Canada goldenrod herb enriched with flavonoids.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".