A Multiplex PCR Method for the Identification of Commercially Important Salmon and Trout Species ( <i>Oncorhynchus</i> and <i>Salmo</i> ) in North America
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to develop a species-specific multiplex polymerase chain reaction (PCR) method that allows for the detection of salmon species substitution on the commercial market. Species-specific primers and TaqMan® probes were developed based on a comprehensive collection of mitochondrial 5' cytochrome c oxidase subunit I (COI) deoxyribonucleic acid (DNA) "barcode" sequences. Primers and probes were combined into multiplex assays and tested for specificity against 112 reference samples representing 25 species. Sensitivity and linearity tests were conducted using 10-fold serial dilutions of target DNA (single-species samples) and DNA admixtures containing the target species at levels of 10%, 1.0%, and 0.1% mixed with a secondary species. The specificity tests showed positive signals for the target DNA in both real-time and conventional PCR systems. Nonspecific amplification in both systems was minimal; however, false positives were detected at low levels (1.2% to 8.3%) in conventional PCR. Detection levels were similar for admixtures and single-species samples based on a 30 PCR cycle cut-off, with limits of 0.25 to 2.5 ng (1% to 10%) in conventional PCR and 0.05 to 5.0 ng (0.1% to 10%) in real-time PCR. A small-scale test with food samples showed promising results, with species identification possible even in heavily processed food items. Overall, this study presents a rapid, specific, and sensitive method for salmon species identification that can be applied to mixed-species and heavily processed samples in either conventional or real-time PCR formats. PRACTICAL APPLICATION: This study provides a newly developed method for salmon and trout species identification that will assist both industry and regulatory agencies in the detection and prevention of species substitution. This multiplex PCR method allows for rapid, high-throughput species identification even in heavily processed and mixed-species samples. An inter-laboratory study is currently being carried out to assess the ability of this method to identify species in a variety of commercial salmon and trout 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 | 0.001 | 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) | 0.001 | 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".