Molecular beacon‐based real‐time PCR method for detection of porcine DNA in gelatin and gelatin capsules
Bibliographic record
Abstract
BACKGROUND: The pharmaceutical industry has boosted gelatin consumption worldwide. This is supported by the availability of cost-effective gelatin production from porcine by-products. However, cross-contamination of gelatin materials, where porcine gelatin was unintentionally included in the other animal sources of gelatin, has caused significant concerns about halal authenticity. The real-time polymerase chain reaction (PCR) has enabled a highly specific and sensitive animal species detection method in various food products. Hence, such a technique was employed in the present study to detect and quantify porcine DNA in gelatin using a molecular beacon probe, with differences in performance between mitochondrial (cytochrome b gene) and chromosomal DNA-(MPRE42 repetitive element) based porcine-specific PCR assays being compared. RESULTS: A higher sensitivity was observed in chromosomal DNA (MPRE-PCR assay), where this assay allows the detection of gelatin DNA at amounts as as low as 1 pg, whereas mitochondrial DNA (CBH-PCR assay) can only detect at levels down to 10 pg of gelatin DNA. When an analysis with commercial gelatin and gelatin capsule samples was conducted, the same result was observed, with a significantly more sensitive detection being provided by the repetitive element of chromosomal DNA. CONCLUSION: The present study has established highly sensitive DNA-based porcine detection systems derived from chromosomal DNA that are feasible for highly processed products such as gelatin and gelatin capsules containing a minute amount of DNA. This sensitive detection method can also be implemented to assist the halal authentication process of various food products available on the market. © 2018 Society of Chemical Industry.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".