Enhancing the antimicrobial and antifungal activities of a coloring extract agent rich in betacyanins obtained from <i>Gomphrena globosa</i> L. flowers
Bibliographic record
Abstract
Although less explored than beetroot (Beta vulgaris L.), the flowers of Gomphrena globosa L. are a very suitable source of betacyanins with strong pigmentation features, together with many other desirable bioactive properties. Thus, the aim of this study was to enhance the antimicrobial and antifungal activities of a pigmented extract obtained from G. globosa flowers by ultrasound assisted extraction (UAE). The procedure was supported with the application of the response surface methodology, a robust optimization technique that allows to study jointly the effects of several variables and responses. To enhance the antimicrobial (Bacillus cereus, Listeria monocytogenes, Escherichia coli and Salmonella typhimurium) and antifungal (Aspergillus flavus, Aspergillus niger, Penicillium ochrochloron and Penicillium verrucosum) activities, the responses were evaluated in terms of the concentrations needed to obtain minimum inhibitory (MIC), minimum bactericidal (MBC) and minimum fungicidal (MFC) concentrations. It was found that the optimal UAE conditions were 10.8 min, 410.5 W, 57.8% of ethanol, and 5 g L-1 of the solid-liquid ratio providing the following response values: (1) from the studied species of bacteria, the MIC ranged from ∼0.15 to 0.35 g L-1 and the MBC ranges were ∼0.30 to 0.65 g L-1; and (2) from the studied fungus species, the MIC ranged from ∼0.20 to 0.30 g L-1 and the MFC ranges were ∼0.40 to 0.65 g L-1. The antibacterial activity dose levels were lower than the antifungal ones. In conclusion, the results obtained in this study highlight extracts from G. globosa flowers as natural sources of betacyanins with application as food colorants with important antimicrobial and antifungal activities.
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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) | 0.000 | 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 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".