Retardation of <i>Listeria Monocytogenes</i> Growth in Mozzarella Cheese Using Antimicrobial Sachets Containing Rosemary Oil and Thyme Oil
Bibliographic record
Abstract
An antimicrobial sachet containing microcellular foam starch (MFS) with embedded rosemary oil and thyme oil was developed to reduce bacterial growth in shredded mozzarella cheese. The efficacy of the volatiles of oils at various concentrations in reducing Listeria monocytogenes as well as the release of the oils from the MFS have been also determined in this study. The cheese, inoculated with a cocktail of 5 strains of L. monocytogenes (approximately 3 log CFU/g), was packaged in a Nylon/EVOH/PE bag. A paper sachet containing MFS embedded with rosemary oil and thyme oil, separately or together, was inserted into the bag. Rosemary and thyme oil volatiles released from the sachet restricted the growth of L. monocytogenes, resulting in a 2.5 log CFU/g reduction on day 9 at 10 °C. The volatile oils also showed inhibitory effects on the growth of lactic acid bacteria (LAB) and total aerobic bacteria (TAB). After 15 d at 10 °C, the numbers of LAB and TAB in the samples containing the sachet with both oils experienced a 1.2 and 1.4 log CFU/g reduction, respectively, compared to untreated samples. Nonetheless, the sachet treatment produced a distinct odor, unfavorably received by the panelists. The results suggest the potential for application of the sachet system for the reduction of growth of L. monocytogenes, LAB, and TAB in food products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".