INFLUENCE OF THE NATURE AND CONCENTRATION OF EXTRACTANTS ON THE EXTRACTION OF FLAVO-NOIDS FROM THE CANADIAN GOLDENROD GRASS
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Bibliographic record
Abstract
The paper presents the results of the study of influence of nature (in particular, dielectric constant and dynamic viscosity) and the concentration of the following extractants: methanol, ethanol, propanol, acetone, dimethylsulfoxide, ethyl acetate, propanol-2, butanol, butanol-2 for the extraction of flavonoids from Canadian goldenrod grass. The content of flavonoids in extracts prepared from the Canadian goldenrod grass was statistically significant (p<0.05) depending on the nature and concentration of the extractants used to prepare them. The most critical factor influencing on the extraction of this group of biologically active substances was the dynamic viscosity of the extractant. It was found that aqueous solutions of extractants have a greater extractive capacity than the corresponding absolute extractants. The maximum amount of flavonoids was extracted with use 80% methanol, 60% ethanol, 40% propanol, 60% acetone and 80% dimethylsulfoxide. From two to seven flavonoids were detected using the high-performance liquid chromatography method in the prepared extracts. The manner of the extraction of the flavonoids sum was predominantly determined by the dominant glycoside – isoquercitrin. Considering the better reproducibility of the extraction of the flavonoids sum, the wide application of ethanol and its aqueous solutions as extractants, the attribution of this extractant to low-toxic solvents and a greater extractive capacity for other classes of phenolic compounds (in particular, hydroxycinnamic acids), 60% ethanol is recommended to use for extraction of flavonoids from the Canadian goldenrod grass.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it