Rapid Detection of E. coli on Goat Meat by Electronic Nose
Bibliographic record
Abstract
Much attention has been paid on the foodborne illness of food, which is easily contaminated with bacteria or pathogens. Escherichia coli (E.coli) is one of these bacteria that commonly live in the contaminated animal meat. There is a growing need in the food industry for pathogen detection systems that are sensitive to low levels of bacteria, specific to the target organisms, capable of yielding results at or near real time. Both contaminated and non-contaminated goat meat were tested using an electronic nose (Cyranose-320) which consists of 32 polymer sensors. We developed an electronic nose method for the rapid detection of E. coli O157:H7 in goat meat. Principal Component Analysis (PCA) method was applied to analyze the experimental data, and the results indicated that they either overlap or are very close making it very difficult for the device to correctly identify. E-nose has a potential for being used as a tool for rapid detection of contamination, although it is not able to detect very low concentration of the contaminant. Keywords: Goat meat; bacteria (E.coli); electronic nose; quality detection
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".