Identification and Quantification of Multiple Pathogenic <i>Escherichia coli</i> Strains Based on a Plasmonic Sensor Array
Bibliographic record
Abstract
Pathogenic Escherichia coli ( E. coli ) is a widespread and clinically significant foodborne pathogen. Due to its high mutation rates and phenotypic diversity, rapid identification of its subtypes remains challenging and prone to false positives when detecting single strains. In this study, we developed a plasmonic sensor array with high-dimensional signal readouts (ζ-potential, dynamic light-scattering (DLS), surface-enhanced Raman scattering (SERS), and ultraviolet–visible (UV–vis) absorption spectra) for the selective discrimination of pathogenic E. coli, integrated with bacterial culture methods. The plasmonic sensor units demonstrated strong encoding capabilities, facilitating the differentiation of subtle variations among various E. coli strains and showing excellent anti-interference performance. The array realized different pathogenic E. coli strains, bacterial mixture identification, and even quantitative detection. Remarkably, the working concentration for the sensor array was notably low, at 10 4 CFU/mL. Finally, by incorporating bacterial isolation culture, the designed sensor array obtained 100% accuracy in detecting E. coli in real food samples. These findings highlight the sensor array’s potential for applications in food safety monitoring and clinical diagnostics, offering a sensitive, rapid, and reliable tool for pathogen detection in complex samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".