Effectual Gold Nanoprobe Sensor for Screening Cow Milk Adulteration in Goat Milk—Comparison With Conventional PCR
Bibliographic record
Abstract
Different methods have been used to detect milk adulteration, but in recent years the use of nanomaterials has been introduced as an interesting tool, due to their optical properties. A gold nanoparticle (AuNP) probe application was developed in order to evaluate milk adulteration. The methodology relies on the colorimetric differentiation and the participation profiles of the hybridization DNA sequence with the AuNPs. Various concentrations, from 0.01 (traces) to 50%, of cows’ milk in goats’ milk samples were prepared for DNA extraction, further identification with the AuNPs and comparison with a conventional PCR. Also, a total of 40 dairy products from goat milk, commonly consumed in Greece, were tested. Negative and goat reaction mixtures showed a purplish coloured solution with a peak at > 570 nm, while samples containing bovine DNA had an absorbance closer to the characteristic peak of the AuNPs at 520-525 nm. Presence of bovine milk was detected even at traces level, achieving a detection level comparable to those achieved by conventional PCR. The use of AuNPs in milk products, provides a low-cost and easy-to-perform method and offers the possibility to detect fraudulent practices in various food matrices.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".