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DNA-based Fish Species Identification Protocol

DNA-based Fish Species Identification Protocol

Published on: April 28, 2010

Preventing illegal seafood trade using machine-learning assisted microbiome analysis.

Luca Peruzza¹, Francesco Cicala¹, Massimo Milan²

¹Department of Comparative Biomedicine and Food Science, University of Padova, Viale Dell'Università 16, Legnaro, 35020, Italy.

|September 10, 2024

Summary

Microbiome profiling coupled with machine learning accurately traces seafood origin, combating fraud. This robust tool validates shellfish traceability, even with seasonal changes and depuration.

Keywords:

Food traceability Illegal unreported unregulated (IUU) fishing Machine learning Manila clam Microbiota 16S North Adriatic sea

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Area of Science:

Marine biology
Genomics
Bioinformatics

Background:

Seafood supply chains face significant fraud risks.
Accurate traceability tools are essential for consumer confidence and preventing illegal trade.
Microbiome profiling (MP) and machine learning (ML) offer a novel approach to seafood origin verification.

Purpose of the Study:

To develop and validate a precise method for tracing the origin of Manila clams using microbiome profiling and machine learning.
To assess the robustness of the method against seasonal variations, inter-annual differences, and depuration processes.
To provide a tool for routine implementation in preventing the trade of illegally harvested or mislabeled shellfish.

Main Methods:

Collected Manila clam samples from various locations and seasons across the Northern Adriatic coast.
Performed DNA extraction and 16S DNA metabarcoding on clam tissues (gills and digestive glands).
Utilized machine learning algorithms on amplicon sequence variants for origin classification, with independent training and testing datasets.

Main Results:

Microbiome profiling coupled with machine learning achieved high accuracy (Cohen K-score > 0.95) in distinguishing clams from a banned area versus farming sites.
Classification of four distinct farming areas showed good accuracy (score 0.76).
The method demonstrated robustness against seasonal and inter-annual variability, as well as depuration treatments.

Conclusions:

Microbiome profiling and machine learning provide an effective and robust tool for tracing shellfish origin.
The developed method is suitable for routine implementation to combat seafood fraud and mislabeling.
This technology enhances the reliability of seafood traceability and supports eco-labelling validation.