Sequence based characterization of microbial communities in food: The panacea for smart detection of food microbes or dirty deeds done dirt cheap?
Bibliographic record
Abstract
Background Sequence-based methods are a rapid and high-throughput approach to characterize microbial communities in food and food processing facilities. Such methodologies have been widely used for characterisation of microbial communities or for detection of specific microbes the past years. They have partially replaced culture-based approaches, but their limitations are often overlooked. Scope of this review This review briefly outlines advantages and limitations of sequence-based methods, which mainly relate to the limited taxonomic resolution of gene amplicon sequencing, PCR bias that distorts quantitative relationships, the lack of differentiation of dead and viable cells, and the completeness and contamination of metagenomic assembled bins. We also provide a perspective on innovative approaches for smart microbial detection that (i) integrate sequence-based methods and (high throughput) culture-based methodology; (ii) use enrichment culture in combination with nanopore sequencing and live basecalling for rapid detection; (iii) use current ecological concepts and up-to-date bioinformatics tools to identify bacterial strains; and (iv) employ hybrid assembly of metagenomics sequences. Conclusions The proposed framework for smart microbial detection makes best use of both world – 19 th century microbiological methods and 21 st century sequencing technology, to improve the accuracy, resolution and sensitivity of detection of pathogens, fermentation microbes and spoilage microbes in food.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".