Rapid Detection of Viable Microorganisms Based on a Plate Count Technique Using Arrayed Microelectrodes
Bibliographic record
Abstract
Development of a miniaturized biosensor system that can be used for rapid detection and counting of microorganisms in food or water samples is described. The developed microsystem employs a highly sensitive impedimetric array of biosensors to monitor the growth of bacterial colonies that are dispersed across an agar growth medium. To use the system, a sample containing the bacteria is cultured above the agar layer. Using a multiplexing network, the electrical properties of the medium at different locations are continuously measured, recorded, and compared against a baseline signal. Variations of signals from different biosensors are used to reveal the presence of bacteria in the sample, as well as the locations of bacterial colonies across the biochip. This technique forms the basis for a label-free bacterial detection for rapid analysis of food samples, reducing the detection time by at least a factor of four compared to the current required incubation times of 24 to 72 hours for plate count techniques. The developed microsystem has the potential for miniaturization to a stage where it could be deployed for rapid analysis of food samples at commercial scale at laboratories, food processing facilities, and retailers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".