Milk Adulteration: Detection of Bovine Milk in Caprine Dairy Products by Real Time PCR
Bibliographic record
Abstract
Milk adulteration is an international social problem. Consumption of adulterated milk may cause serious health problems and a great concern of the food industry has been raised. In this study, a method based on the polymerase chain reaction (PCR) principle was validated for detecting cow’s milk in goat's dairy products. A total of 40 goat's dairy products commonly consumed in Greece, were tested. Various concentrations, from 0.01 to 90%, of cows’ milk in goats’ milk samples were prepared for DNA extraction and further PCR analysis. Selection of highly polymorphic regions within the cow and goat mitochondrial D-loops, showing low homology between the two species, allowed to choose specific primer pairs for detection of cow and goat DNA. After electrophoresis, cow DNA was characterised by the fragment of the size of 300 bp, goat DNA by the fragment of 444 bp. The detection limit of the PCR method was 0.01% while sensitivity and specificity of the method were both 100%. Goat dairy products samples were tested for the presence of cow DNA. Thirty six out of forty (90%) that were tested, were found to produce cow-specific PCR product in addition to goat PCR product while only two samples gave goat-specific product only. The results are disappointing in terms of the food labelling honesty but on the other hand PCR is again a quickly, easy and reliable method that could be used for extended adulteration screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".