Evaluation of Petrifilm™ EC method for enumeration of<i>E. coli</i>from soil
Bibliographic record
Abstract
AIMS: To evaluate the suitability of commercially available Petrifilm EC plates for enumeration of Escherichia coli from soil. METHODS AND RESULTS: A confirmed E. coli strain isolated from liquid swine manure was inoculated into sterilized sandy clay loam and loam soils at the concentrations of 10(2), 10(3), 10(5) CFU g(-1) of soil. The efficiency of recovery on Petrifilm EC plates for soils spiked with E. coli was compared with standard membrane filtration techniques on m-FC basal medium supplemented with 3-bromo-4-chloro-5-indoyl-beta-D-glucopyranoside (BCIG) and most probable numbers (MPN) techniques in E. coli medium with 4-methylumbelliferyl-beta-D-glucuronide (EC-MUG) broth. Petrifilm EC and m-FC (BCIG) methods were then assessed for the ability to recover E. coli from field soils applied with swine manure. No significant differences (P > 0.05) were observed between Petrifilm EC, m-FC (BCIG) and MPN methods for the recovery of E. coli from spiked samples, irrespective of soil type. However, recovery of E. coli from manure-applied field soil samples showed a significant difference (P < 0.05) between the Petrifilm EC method and the m-FC method in enumerating E. coli possibly as a result of false positives on m-FC. CONCLUSION: The Petrifilm EC method is suitable for the enumeration of E. coli from soil with a detection limit of 10 CFU g(-1) soil. SIGNIFICANCE AND IMPACT OF THE STUDY: The commercially available Petrifilm EC method is comparatively low cost, easy to use method for the enumeration of E. coli from soil without the need for further confirmation tests.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".