Application of lysozyme and nisin to control bacterial growth on cured meat products
Bibliographic record
Abstract
Chemical preservatives are increasingly unacceptable to consumers, while demand is increasing for minimally processed and convenient food products. Response to this situation requires the development of novel preservation strategies. Potential alternatives to traditional chemical preservatives are the enzymes lysozyme and nisin, which can be perceived by consumers as natural, due to their biological origin. Reports published by other authors have indicated that interaction between lysozyme or nisin with chelators may result in an increased antimicrobial effect against Gram positive and Gram negative organisms. Experiments were conducted in nutrient broth using organisms of concern for safety or spoilage reasons in cured meat products. The individual antimicrobial effect of lysozyme, nisin, ethylene diamine tetraacetate (EDTA), tripolyphosphate and diacetyl was determined. A response surface analysis of fractional inhibitory concentration data was conducted to determine what, if any, interactions occurred between lysozyme and the other agents, and to determine if lysozyme potentiated the action of any of the other antimicrobials. (Abstract shortened by UMI.)
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) | 0.001 | 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".