Effect of interaction with food constituents on plant extracts antibacterial activity
Bibliographic record
Abstract
Gaillac red wine powder and Cinnamon cassia essential oil were selected for their in vitro antibacterial activity against Staphylococcus aureus CNRZ3 and Listeria innocua LRGIA 01, respectively. In order to assess the potential application of Gaillac wine powder to the preservation of raw meat, its antibacterial activity was assayed in Mueller Hinton broth supplemented with up to 20% (w/w) bovine meat proteins (bovine meat protein content): Gaillac wine powder as well as resveratrol, a stilbene polyphenol present in red wine, lost their antibacterial activity, likely as a result of interactions of Gaillac wine antibacterial molecules with bovine meat proteins at the expense of their interactions with S. aureus CNRZ3 cells. Cinnamon cassia essential oil antibacterial activity assays in Tryptone Soya broth, skimmed, semi-skimmed, and whole milk showed that its antibacterial activity was significantly reduced by milk fat globules but not by milk proteins: it could thus be used for the preservation of skimmed milk. The developed methodology based on the use of microbiological media mimicking the composition of perishable foods or of liquid foods such as sterilized milk with various milk fat contents could be used for the rapid screening of antibacterial plant extracts of interest for perishable foods preservation.
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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".