Lock-In Amplified Fluorescence Spectroscopy in a Digital Microfluidic Configuration for Antibiotic Detection of Ciprofloxacin in Milk
Bibliographic record
Abstract
Antibiotic detection in dairy is crucial, as introduction of antibiotics in food can lead to antibiotic resistance and cause allergic reactions in consumers. Dairy farmers risk monetary fines for shipping contaminated milk. Microfluidics is an appealing technology, as it is portable, can be implemented on-site, and is highly automated. This work presents a digital microfluidic dairy device for antibiotic detection. The digital microfluidic dairy device explores integration of a lock-in amplifier with droplet-based microfluidic techniques, being electrowetting actuation and droplet control. The lock-in amplifier setup is initially demonstrated for the fluorescent dye rhodamine B, to establish baseline operation. Ultimately, the lock-in amplifier setup is demonstrated for detection of ciprofloxacin in milk. The limit of detection is significantly lowered through the integration of the lock-in amplifier technology. Actuation of milk and water samples is shown, demonstrating full two dimensional control over the position of water and milk droplets, to enable a complete lab-on-a-chip system. The results show promise for modern dairy testing of antibiotic signatures. [2022-0120]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".