ANTILISTERIAL ACTIVITY OF CBD FOR THE PREVENTION OF LISTERIA MONOCYTOGENES IN DAIRY PRODUCTS
Bibliographic record
Abstract
Research background. Herbal antimicrobials exist in plants and their derived compounds. One such compound is cannabidiol (CBD) extracted from the plant, Cannabis Sativa, also referred to as hemp or marijuana, has drawn interest for its alleged antibacterial and antioxidant abilities. With the increasing problem of antibiotic resistance and the desire to reduce antibiotics in the food industry, researchers are exploring the potential of cannabinoids as an alternative antimicrobial agent. The use of cannabidiol (CBD) in food and beverage products is a growing trend, it is also important for manufacturers to approach this new area with more studies. Milk is a key component in the production of most dairy produce. The products made from milk usually undergo various processes such as pasteurization, fermentation, curdling, and aging to create the final product. This study aims to examine the potential of CBD isolate as a antimicrobial agent which can be used in dairy products to reduce microbial growth and extend their shelf life. Experimental approach. This paper investigated the antilmicrobial properties of CBD against L. monocytogenes in milk as the key solvent to perform studies. The minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of CBD in TSB was carried out at 37 °C in 24 h. CBD’s antilisterial activity was found for whole milk and skim milk at 4°C for 3 days by analyzing their respective growth and kill curves. Results and conclusions Both the MIC and MBC for L. monocytogenes was found to be 2 µg/mL measured by dilution series and plating, respectively. CBD significantly slowed the growth of listeria populations in milk but its effectiveness of CBD was dependant on the fat content of milk. In this paper, we have discussed the potential for using and improving CBD as a preservative to combat L. monocytogenes in dairy products that use milk as a primary ingredient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".