Effectual Gold Nanoprobe Sensor for Screening Horse Adulteration in Meat Products
Bibliographic record
Abstract
A gold nanoparticle (AuNP) probe strategy for testing meat authenticity was developed, which relies on the colorimetric differentiation of a particular DNA sequence, due to the differential aggregation profiles exhibited by the AuNPs in the presence or absence of specific target hybridization. Gold nanoparticles were conjugated with thiolated oligonucleotides for specifically identifying a 69 bp fragment of the horse cytochrome b gene. In the presence of a complementary target preventing aggregation of the AuNPs when acid was added, the reaction mixtures retained the original pink colouration of the colloidal particles, whereas they turned purple in the opposite event. Fresh meatballs, prepared using pure bovine meat, were used as blanks, producing a purplish coloured solution with a peak at ≥570nm. Horse meat was used as positive control and the pink colour obtained after hybridization exhibited maximum absorption at 524 nm. Both the specificity and sensitivity of the tests performed were 100%. Visual observations and spectroscopic data indicated that the coloration produced by the AuNPs (positive-pink, negative-purple) was very stable, showing no change under normal laboratory conditions. The use of AuNPs for the colorimetric detection of DNA targets from undeclared species in meat products provides an inexpensive and easy-to-perform alternative to common molecular assays. The technology described here can be further developed to accommodate detection of many cases of adulteration and fraudulent practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".