Rapid Screening for Gram-Negative and Gram-Positive Beer-Spoilage <i>Firmicutes</i> Using a Real-Time Multiplex PCR
Bibliographic record
Abstract
Current methods for detection and identification of beer-spoilage bacteria can be time-consuming and may not encompass all beer-spoilage isolates due to targeting of specific species. As such, a rapid method that targets a broader spectrum of beer-spoilage bacteria is likely to be more efficient for initial detection of contamination. Building on our previous real-time PCR (rltPCR) that detects Firmicutes, we created a system that enables concurrent detection and differentiation of gram-negative and -positive brewery-associated Firmicutes. Our two previously described rltPCR hydrolysis probes, which are able to detect all bacteria and Firmicutes, were used in combination with a newly developed probe (GmNeg) that detects only gramnegative brewery-associated Firmicutes. In silico analysis performed to determine the specificity of the GmNeg probe predicted that the probe would detect all gram-negative brewery-associated Firmicutes. This was confirmed by rltPCR analysis of brewery-associated bacteria, with the GmNeg probe showing specificity for gram-negative Firmicutes but not for gram-positive Firmicutes or any non-Firmicutes. The sensitivity of this rltPCR system was 35 fg of DNA per reaction, corresponding to approx. 10–20 bacteria. This multiplex rltPCR will enable brewery quality control laboratories to rapidly screen for brewery-associated Firmicutes, with identification of a contaminant as either a gram-negative or -positive bacterium.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".