Antimicrobial Efficacy of Natural Phenolic Compounds against Gram Positive Foodborne Pathogens
Bibliographic record
Abstract
Protection of food from pathogens and spoilage organisms has been achieved by a variety of methods. Due to consumer preference, health and economic concerns in recent years, there is considerable interest to employ natural antimicrobials as an alternative to control the growth of microorganisms. This study evaluates the antimicrobial efficacy of natural plant derived phenolic compounds (PDPC) including chlorogenic acid, coumarin, curcumin, ellagic acid, (-) epicatechin, eugenol, rosmarinic acid, rutin, tannic acid, thymol, thymoquinone, and xanthohumol) as preservatives in food products. Several strains of Bacillus, Listeria and Clostridium species were treated with 12 natural PDPCs. Concentrations of 5, 10, 15, and 20 ppm of each compound were evaluated by broth micro-dilution method and the MICs were determined by using optical density after 24 and 60 hours of incubation. Thymoquinone, xanthohumol and ellagic acid demonstrated the highest antimicrobial efficacy (MIC <20 ppm). Structural alterations in treated bacteria were observed via scanning electron microscopy. The results demonstrated that the PDPCs have varying antimicrobial activities against both aerobic and anaerobic Gram-positive foodborne pathogens following 24 hour and 60 hour incubation periods, respectively. Natural sources of phenolic compounds contain major antimicrobial components and have great potential to control the growth of pathogens and be used as natural antimicrobials and food preservatives for extended storage. This study highlighted the antimicrobial efficacy of some PDPCs which may replace the artificial antimicrobials and preservatives in food industry to partially or completely control or inhibit the growth of harmful bacteria.
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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".