Staphylococcus aureus Detection in Milk Using a Thickness Shear Mode Acoustic Aptasensor with an Antifouling Probe Linker
Bibliographic record
Abstract
Contamination of food by pathogens can pose a serious risk to health. Therefore, monitoring for the presence of pathogens is critical to identify and regulate microbiological contamination of food. In this work, an aptasensor based on a thickness shear mode acoustic method (TSM) with dissipation monitoring was developed to detect and quantify Staphylococcus aureus directly in whole UHT cow’s milk. The frequency variation and dissipation data demonstrated the correct immobilization of the components. The analysis of viscoelastic properties suggests that DNA aptamers bind to the surface in a non-dense manner, which favors the binding with bacteria. The aptasensor demonstrated high sensitivity and was able to detect S. aureus in milk with a 33 CFU/mL limit of detection. Analysis was successful in milk due to the sensor’s antifouling properties, which is based on 3-dithiothreitol propanoic acid (DTTCOOH) antifouling thiol linker. Compared to bare and modified (dithiothreitol (DTT), 11-mercaptoundecanoic acid (MUA), and 1-undecanethiol (UDT)) quartz crystals, the sensitivity of the sensor’s antifouling in milk improved by about 82–96%. The excellent sensitivity and ability to detect and quantify S. aureus in whole UHT cow’s milk demonstrates that the system is applicable for rapid and efficient analysis of milk safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".