Effect of Essential Oils and Vacuum Packaging on Spoilage-Causing Microorganisms of Marinated Camel Meat during Storage
Bibliographic record
Abstract
The use of essential oils (EOs) and/or vacuum packaging (VP) with meats could increase product shelf-life. However, no studies investigating the effect of EOs and VP on camel meat background microbiota have been conducted previously. The study aimed to analyze the antimicrobial effect of essential oils (EOs) carvacrol (CA), cinnamaldehyde (CI), and thymol (TH) at 1 or 2% plus vacuum packaging (VP) on the growth of spoilage-causing microorganisms in marinated camel meat chunks during storage at 4 and 10 °C. VP is an effective means to control spoilage in unmarinated camel meat (CM) and marinated camel meat (MCM) compared to aerobic packaging (AP). However, after EO addition to MCM, maximum decreases in spoilage-causing microorganisms were observed under AP on day 7. Increasing the temperature from 4 to 10 °C under AP increased the rate of spoilage-causing bacterial growth in CM and MCM; however, EOs were more effective at 10 °C. At 10 °C the maximum reductions in total mesophilic plate counts, yeast and molds, mesophilic lactic Acid bacteria, Enterobacteriaceae, and Pseudomonas spp. were 1.2, 1.4, 2.1, 3.1, and 4.8 log CFU/g, respectively. Incorporating EOs at 2% in MCM, held aerobically under temperature abuse conditions, delayed spoilage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".