ВЛИЯНИЕ ПРЕДВАРИТЕЛЬНОЙ ОБРАБОТКИ НА СОДЕРЖАНИЕ ФЛАВОНОИДОВ ПРИ ПОЛУЧЕНИИ ЭКСТРАКЦИОННЫХ ЛЕКАРСТВЕННЫХ ФОРМ ЗОЛОТАРНИКА КАНАДСКОГО ТРАВЫ
Bibliographic record
Abstract
Предпосылки. Предварительная обработка лекарственного растительного сырья повышает выход биологически активных веществ (в частности флавоноидов) при экстракции, что целесообразно использовать для получения экстракционных лекарственных форм золотарника канадского травы. Целью работы является установление влияния предварительной обработки на выход флавоноидов из золотарника канадского травы при разработке технологических параметров получения настоек и экстрактов. Методы. Объектом исследования служила золотарника канадского трава. Изучали четыре варианта предобработки: термообработка, обезжиривание и их комбинации в двух вариантах. Результаты. Наибольший выход флавоноидов в настойки наблюдали при объемной доле этанола – 60–70 %, соотношении сырья и экстрагента – 1 г к 25, степени измельчения сырья – 2000 мкм, времени отстаивания первичной вытяжки – не более 4 дней при получении ремацерацией по три дня.Наибольшее содержание флавоноидов в сухих экстрактах отмечено при относительном объеме отгонки – 90 %, температуре отгонки – 80 °С, минимальном времени отгонки – в течение 40 мин, толщине отгоняемого слоя – 6 см, времени отстаивания первичной вытяжки – не более 4 дней.Наибольший выход флавоноидов в настойку наблюдается при термообработке золотарника канадского травы, в сухой экстракт – при обезжиривании самого ЛРС. Выводы. Технологии получения настоек и сухих экстрактов, разработанные с учетом этапа предобработки, могут быть использованы для получения указанных экстракционных лекарственных форм, обогащенных флавоноидами золотарника канадского травы. Prerequisites. Pre-treatment of medicinal plant signal increases the yield of biologically active substances (in particular, flavonoids) during extraction, which is a leader for obtaining extraction medicinal forms of Canadian goldenrod herbs. The aim of the work is to establish the effect of pre-treatment on the flavonoids yield from Canadian goldenrod herb when developing technological parameters for obtaining tinctures and extracts. Method. The object of the study was Canadian goldenrod herb. Four pre-treatment options were studied: heat pre-treatment, defatting and their combinations in two options. Results. The highest yield of flavonoids in tinctures was observed with a volume fraction of ethanol of 60–70 %, a ratio of raw materials and extractant of 1 g to 25, a degree of grinding of raw materials of 2000 ?m, and a settling time of the primary extract of no more than 4 days when obtained by remaceration for three days. The highest content of flavonoids in dry extracts is noted with a relative distillation volume of 90 %, distillation temperature of 80 °C, minimum distillation time of 40 min, thickness of the distilled layer of 6 cm, and settling time of the primary extract of no more than 4 days. The highest yield of flavonoids in the tincture is observed during heat pre-treatment of Canadian goldenrod herb, in the dry extract – during defatting of the medicinal plant material itself. Conclusions. Technologies for obtaining tinctures and dry extracts, developed taking into account the pre-treatment stage, can be used to obtain the specified extraction dosage forms enriched with flavonoids of Canadian goldenrod herb.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".