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

Background: Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood traceability are needed. Here we show that the use of microbiome profiling (MP) coupled with machine learning (ML) allows precise tracing the origin of Manila clams harvested in areas separated by small geographic distances.

Temporal occurrence of Cryptosporidium in the Manila clam Ruditapes philippinarum in northern Adriatic Italian lagoons.

In order to evaluate the temporal occurrence of Cryptosporidium oocysts in Ruditapes philippinarum clams bred along the northeastern Italian Adriatic coast and molecularly characterize the isolates, 2,160 specimens (180 clams per month) were collected from three clam farms from January to December 2004. Two farms (sites A and B) were located in Venice (Chioggia, Veneto region) and one (site C) in the Marano Lagoons (Friuli Venezia Giulia region). Clams from 36 pools (i.