High-throughput analysis of microbiomes in a meat processing facility: are food processing facilities an establishment niche for persisting bacterial communities?
Bibliographic record
Abstract
BACKGROUND: Microbial spoilage in meat impedes the development of sustainable food systems. However, our understanding of the origin of spoilage microbes is limited. Here, we describe a detailed longitudinal study that assesses the microbial dynamics in a meat processing facility using high-throughput culture-dependent and culture-independent approaches to reveal the diversity, dispersal, persistence, and biofilm formation of spoilage-associated microbes. RESULTS: Culture-dependent and culture-independent approaches revealed a large diversity of microbes within the meat facility, including 74 undescribed bacterial taxa and multiple spoilage-associated microbes. Ten out of 10 reconstituted microbial communities formed biofilms, and the biofilm biomass was generally higher at 4 °C than at 25 °C. Isolates obtained at different sampling times or from different sampling sites that differed in fewer than 10 genome-wide single-nucleotide polymorphisms were considered the same (persistent) strains. Strains of Carnobacterium maltaromaticum and Rahnella rivi persisted over a period of 6 months across sampling sites and time, stemming from floor drains in the cooler room. Meat isolates of Carnobacterium divergens, Rahnella inusitata, and Serratia proteamaculans originated from food contact and non-food contact environments of the packaging area. CONCLUSIONS: Culture-dependent isolation, complemented by culture-independent analyses, is essential to fully uncover the microbial diversity in food processing facilities. Microbial populations permanently resided within the meat processing facility, serving as a source of transmission of spoilage microbes. The ability of these microbes to coexist and form biofilms facilitates their persistence. Our data together with prior data on persistence of Listeria monocytogenes indicates that microbial persistence in food processing facilities is the rule rather than an exception. Video Abstract.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".