DNA barcoding for the verification of supplier’s compliance in the seafood chain: How the lab can support companies in ensuring traceability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it