A DNA Mini-Barcoding System for Authentication of Processed Fish Products
Bibliographic record
Abstract
Species substitution is a form of seafood fraud for the purpose of economic gain. DNA barcoding utilizes species-specific DNA sequence information for specimen identification. Previous work has established the usability of short DNA sequences-mini-barcodes-for identification of specimens harboring degraded DNA. This study aims at establishing a DNA mini-barcoding system for all fish species commonly used in processed fish products in North America. Six mini-barcode primer pairs targeting short (127-314 bp) fragments of the cytochrome c oxidase I (CO1) DNA barcode region were developed by examining over 8,000 DNA barcodes from species in the U.S. Food and Drug Administration (FDA) Seafood List. The mini-barcode primer pairs were then tested against 44 processed fish products representing a range of species and product types. Of the 44 products, 41 (93.2%) could be identified at the species or genus level. The greatest mini-barcoding success rate found with an individual primer pair was 88.6% compared to 20.5% success rate achieved by the full-length DNA barcode primers. Overall, this study presents a mini-barcoding system that can be used to identify a wide range of fish species in commercial products and may be utilized in high throughput DNA sequencing for authentication of heavily processed fish products.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".