Recent Advancements in Enhancing Antimicrobial Activity of Plant-Derived Polyphenols by Biochemical Means
Bibliographic record
Abstract
Plants are a reservoir of phytochemicals, which are known to possess several beneficial health properties. Along with all the secondary metabolites, polyphenols have emerged as potential replacements for synthetic additives due to their lower toxicity and fewer side effects. However, controlling microbial growth using these preservatives requires very high doses of plant-derived compounds, which limits their use to only specific conditions. Their use at high concentrations leads to unavoidable changes in the organoleptic properties of foods. Therefore, the biochemical modification of natural preservatives can be a promising alternative to enhance the antimicrobial efficacy of plant-derived compounds/polyphenols. Amongst these modifications, low concentration of ascorbic acid (AA)–Cu (II), degradation products of ascorbic acid (DPAA), Maillard reaction products (MRPs), laccase–mediator (Lac–Med) and horse radish peroxidase (HRP)–H2O2 systems standout. This review reveals the importance of plant polyphenols, their role as antimicrobial agents, the mechanism of the biochemical methods and the ways these methods may be used in enhancing the antimicrobial potency of the plant polyphenols. Ultimately, this study may act as a base for the development of potent antimicrobial agents that may find their use in food applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".