Twenty‐three years of PCR‐based seafood authentication assay development: What have we learned?
Bibliographic record
Abstract
Seafood is a prime target for fraudulent activities due to the complexity of its supply chain, high demand, and difficult discrimination among species once morphological characteristics are removed. Instances of seafood fraud are expected to increase due to growing demand. This manuscript reviews the application of DNA-based methods for commercial fish authentication and identification from 2000 to 2023. It explores (1) the most common types of commercial fish used in assay development, (2) the type of method used, (3) the gene region most often targeted, (4) provides a case study of currently published assays or primer-probe pairs used for DNA amplification, for specificity, and (5) makes recommendations for ensuring standardized assay-based reporting for future studies. A total of 313 original assays for the detection and authentication of commercial fish species from 191 primary articles published over the last 23 years were examined. The most explored DNA-based method was real-time polymerase chain reaction (qPCR), followed by DNA sequencing. The most targeted gene regions were cytb (cytochrome b) and COI (cytochrome c oxidase 1). Tuna was the most targeted commercial fish species. A case study of published tuna assays (n = 19) targeting the cytb region found that most assays were not species-specific through in silico testing. This was conducted by examining the primer mismatch for each assay using multiple sequence alignment. Therefore, there is need for more standardized DNA-based assay reporting in the literature to ensure specificity, reproducibility, and reliability of results. Factors, such as cost, sensitivity, quality of the DNA, and species, should be considered when designing assays.
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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 | 0.048 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".