DNA barcoding for the verification of supplier’s compliance in the seafood chain: How the lab can support companies in ensuring traceability
Bibliographic record
Abstract
Food Business Operators (FBOs) rely on laboratory analysis to ensure seafood traceability. DNA barcoding and Forensically Informative Nucleotide Sequencing may represent a support within self-checking programs finalized to suppliers’ qualification and products identity certification. The present study aimed at verifying the usefulness of a decisional procedure (decision tree) set up at the FishLab (Department of Veterinary Sciences, University of Pisa, Italy) for seafood species identification by DNA analysis, to cope with FBOs’ needs. The decision tree was applied to the analysis of 182 seafood (fish and molluscs) products, conferred to the FishLab by different FBOs between 2014 and 2015 as result of their self-checking activities. The analysis relied on a standard COI gene fragment eventually integrated by the analysis of alternative or supportive molecular targets (cytb and 16S rRNA). It also included a mini-DNA barcoding approach for processed products. Overall, 96.2% of the samples were unambiguously identified at species level using the elective target alone (92.4%) or a multitarget approach (3.8%). The lack of species identification (3.8%) was attributable to the absence of reference sequences or to the low resolution of the molecular targets. Nonetheless, all the molecular results were deemed adequate to evaluate the sample’s compliance to the label information. Noncompliances were highlighted in 18.1% of the products. The protocol was proven as an effective supportive tool for the seafood identity verification within the supply chain self-checking activities. In addition, a considerable fraud rate was confirmed and the species most frequently involved in substitution were pointed out.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".