A Meta-Analysis of Bacterial Communities in Food Processing Facilities: Driving Forces for Assembly of Core and Accessory Microbiomes across Different Food Commodities
Bibliographic record
Abstract
Microbial spoilage is a major cause of food waste. Microbial spoilage is dependent on the contamination of food from the raw materials or from microbial communities residing in food processing facilities, often as bacterial biofilms. However, limited research has been conducted on the persistence of non-pathogenic spoilage communities in food processing facilities, or whether the bacterial communities differ among food commodities and vary with nutrient availability. To address these gaps, this review re-analyzed data from 39 studies from various food facilities processing cheese (n = 8), fresh meat (n = 16), seafood (n = 7), fresh produce (n = 5) and ready-to-eat products (RTE; n = 3). A core surface-associated microbiome was identified across all food commodities, including Pseudomonas, Acinetobacter, Staphylococcus, Psychrobacter, Stenotrophomonas, Serratia and Microbacterium. Commodity-specific communities were additionally present in all food commodities except RTE foods. The nutrient level on food environment surfaces overall tended to impact the composition of the bacterial community, especially when comparing high-nutrient food contact surfaces to floors with an unknown nutrient level. In addition, the compositions of bacterial communities in biofilms residing in high-nutrient surfaces were significantly different from those of low-nutrient surfaces. Collectively, these findings contribute to a better understanding of the microbial ecology of food processing environments, the development of targeted antimicrobial interventions and ultimately the reduction of food waste and food insecurity and the promotion of food sustainability.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".