A lock-in amplifier biosensor for dairy applications
Bibliographic record
Abstract
Antibiotic exposure can cause the development of antibiotic resistant bacteria and can induce allergic reactions in humans. A source of high antibiotic exposure is contaminated dairy milk. To prevent contaminated dairy milk, development of antibiotic biosensors for on-site detection is required. This is particularly important for dairy farmers as fines and suspension of license are consequences of shipping contaminated milk to processing plants, where antibiotic tests are currently performed. There are also environmental and economic consequences when whole dairy tanks are contaminated and go to waste. Our work addresses this problem by developing an antibiotic biosensor for farmers to test their milk on-site for ciprofloxacin prior to sending to processing plants. Ciprofloxacin is frequently used to treat common bacterial infections in cattle. Our work provides the following contributions. We introduce an antibiotic biosensor that integrates fluorescence spectroscopy, microfluidic processing, and lock-in amplification to improve the limit-of-detection of ciprofloxacin below the regulatory limit for milk. We also perform traditional fluorescence detection for comparison. Our antibiotic biosensor has a signal-flow starting with an ultraviolet light emitting diode for illumination of ciprofloxacin, and moving through a microfluidic platform, a photodiode for detection of the fluorescent wavelength, and a lock-in amplifier. Our antibiotic biosensor is well-suited for fast on-site analyses and is designed for ease-of-use. Overall, our work shows promise for the integration of real-time on-site antibiotic detection of antibiotics in dairy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".