PREPARATION AND ANALYSIS OF PHYTOPREPARATIONS FROM THE WALNUT SEPTUM BIOMASS
Bibliographic record
Abstract
Recently, foreign researchers have been actively working on studying Juglans regia L. septum, which indicates their rich phytochemical composition and a wide range of biological properties. It has been shown that extracts from walnut septum have high antioxidant, antitumor, and antiflogistic activity, as well as antimutagenic, regenerating, and bactericidal potential. The work aimed to obtain a natural complex of phenolic compounds from the biomass of walnut septum of selected varieties to study the phytochemical composition of the obtained phytopreparations and their antioxidant properties. To extract phenolic compounds, samples of the biomass of walnut septum were mixed with an extraction mixture in a ratio of 1:30 by weight and subjected to ultrasonic treatment at 100 W with a frequency of 20–25 kHz for 10 minutes. The resulting aqueous ethanol extracts were condensed under vacuum, and their physicochemical characteristics and antioxidant properties were determined. The identification and analysis of phenolic compounds in the studied phytopreparations was done using the HPLC-MS/MS method. Seventeen phenolic compounds were found in the composition of the obtained phytoextracts, most of which belong to flavonoids. It was found that the total content of identified flavonoids and their glycosides in aqueous ethanol extracts obtained from the biomass of Juglans regia L. septum was 13.54 mg/g in a thick extract of phenolic compounds – 119.75 mg/g. The total content of the identified phenolic acids in aqueous-ethanol extracts from the biomass of the septum was 4.22 mg/g, in the thick extract – 69.43 mg/g. The antioxidant activity of the studied phytopreparations amounted to 94% inhibition of the DPPH radical. The results of the analysis and identification of the phytochemical composition indicate that the obtained phytopreparations from the biomass of walnut septum are a valuable source of phytomicronutrients as part of functional food ingredients and can also be considered pharmaceutical substances for therapeutic use.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".