Exploring the spoilage microbiota of raw and processed meats under different storage environments and its related profiling methods: a review
Bibliographic record
Abstract
Raw and processed meats are potentially rich ecosystems colonized by a variety of microorganisms whose growth and viability are strongly dependent on storage conditions. Furthermore, for their own adaptations, microorganisms can release exocellular compounds which contribute to meat spoilage due to changes in physicochemical parameters according to preservation methods. Therefore, deeper knowledge of such microorganisms at species level might help to better determine and standardize the best preservation system that may delay the spoilage and extend the meat shelf-life. For this purpose, culture-independent methods have emerged as complementary to culture-dependent methods for a better knowledge at species level of viable spoilage microorganisms in meat ecosystems stored under various conditions. This review reported the most applied methods to identify meat spoilage microbiota and highlighted their limitations in raw and processed meat ecosystems. The use of next generation sequencing (NGS)-based metabarcoding is revealed to be among the most effective techniques to completely profile the microbial ecosystem of processed and stored meats in different environments. Recommendations are formulated to combine NGS tools with an artificial intelligence approach such as machine learning through convolutional neural networks to predict spoilage and monitor meat quality samples in real time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".