Study of plant microcapsulated extracts preparation intended for use in cosmetics and functional foods technology
Bibliographic record
Abstract
Introduction. Introduction Medicinal plants play an important role in medicine, especially in traditional systems, where plant extracts account for a third of all remedies, especially for the treatment of skin diseases. In recent decades, there has been an increased interest in their bioactive components, such as flavonoids and terpenoids, due to their antioxidant, anti-inflammatory, and immunoregulatory properties. These extracts are effective in combating age-related skin changes and improving skin appearance and function. They are used in over-the-counter medications, dietary supplements, and food products for the prevention of chronic diseases. Modern research confirms their effectiveness in the treatment of dermatological diseases. Extracts of aloe, chamomile, and calendula are used in cosmetics, while extracts of turmeric and green tea are used in the food industry as antioxidant additives. Materials and methods. Materials and methods. The aim of the present study is a comprehensive study of the effect of the hydro-modulus (the ratio "raw material (g): extractant (ml)") on the physical and chemical characteristics of extracts of biologically active compounds (BAS) obtained from various plant sources, as well as the optimization of the parameters of microencapsulation of sodium alginate. The following objects were selected as part of the study: Calendula officinalis L. flowers, Salvia officinalis L. leaves, Yarrow grass (Achillea millefolium L.), and the aerial part of Callisia fragrans. The extraction parameters were standardized as follows: temperature 50 °C, hydro-moduli 1:5, 1:10 and 1:15, 2-hour maceration method using distilled water. Sodium alginate and calcium chloride were used for microencapsulation. Conclusion. Research has shown that microencapsulated extracts of calendula and sage are promising for cosmetics and functional food products due to their high bioavailability and stability of active components. Microencapsulation improves the physical and chemical characteristics of the extracts, increasing their solubility and dispersion. This enhances the consumer properties of the products. The extracts have antioxidant, anti-inflammatory, and regenerative properties, as well as contain biologically active substances that have a positive impact on health. The introduction of microencapsulated extracts opens up new opportunities for creating innovative products that meet modern market requirements.
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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) | 0.000 | 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) | 0.001 | 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".