Determination of norfloxacin residues in foods by exploiting the coffee‐ring effect and paper‐based microfluidics device coupling with smartphone‐based detection
Bibliographic record
Abstract
By utilizing the coffee-ring effect and microfluidic paper-based analytical devices (µPADs), this study improved the sensitivity of the determination of norfloxacin in four different food matrices. Micro-PADs in this study were fabricated by designing and embedding wax channels onto cellulose-based filter paper through printing and subjecting the paper to heat to allow the wax to penetrate the paper. Determination of norfloxacin concentration in food samples was achieved by monitoring the colorimetric reaction that occurred between norfloxacin and the added iron (III) nitrate nonahydrate in 5 mM ammonia in each reaction chamber. A transition metal hydroxide was formed through this reaction that resulted in the formation of a solid precipitate to enable the antibiotic to bind to the iron molecule via coordination chemistry. This metal ion-antibiotic complex generated a visible color change. Following the colorimetric reaction, images were taken and subsequently analyzed via ImageJ to determine the relative pixel intensity that was used to infer norfloxacin concentration. The analytical sensitivity of this device was determined to be as low as 50 ppm when analyzing the inner-ring reaction, and as low as 5 ppm when analyzing the outer coffee ring thereby allowing for an alternative cheaper, faster, and more user-friendly method to detect norfloxacin than the conventional methods. PRACTICAL APPLICATION: This novel paper-based microfluidic device can achieve the detection of antibiotic residues in agrifoods in a faster, cheaper, and more user-friendly manner.
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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) | 0.000 | 0.001 |
| 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) | 0.001 | 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".