<i>horA</i>-Specific Real-Time PCR for Detection of Beer-Spoilage Lactic Acid Bacteria
Bibliographic record
Abstract
Beer-spoilage bacteria have long been a problem for brewers. Among the most problematic beer spoilers are several species of the Gram-positive genera Lactobacillus and Pediococcus. Current methods of detecting and identifying these organisms are time-consuming and do not differentiate between bacteria capable of spoiling beer and benign bacteria. The horA-specific real-time polymerase chain reaction (rPCR) described here identifies beer-spoilage organisms based not on their identity, but on the presence of a gene that we show to be highly correlated with the ability of an organism to grow in beer. The horA hop-resistance gene has been shown to be associated with beer spoilage by isolates from four Lactobacillus spp. and one Pediococcus sp. We document the presence of the horA gene in one additional genus and 11 additional species, with many of these bacteria commonly found as beer spoilers. The use of horA-specific rPCR allows for a substantial reduction in the time required for detection of potential beer spoilage bacteria and efficiently discriminates between those organisms that have the horA gene (highly likely to spoil beer) and those organisms that do not have the gene (much less likely to spoil beer). © 2007 American Society of Brewing Chemists, Inc.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".