Preventing illegal seafood trade using machine-learning assisted microbiome analysis

Abstract 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...

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Ngā kaituhi matua:	Luca Peruzza, Francesco Cicala, Massimo Milan, Giulia Dalla Rovere, Tomaso Patarnello, Luciano Boffo, Morgan Smits, Silvia Iori, Angelo De Bortoli, Federica Schiavon, Aurelio Zentilin, Piero Fariselli, Barbara Cardazzo, Luca Bargelloni
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	BMC 2024-09-01
Rangatū:	BMC Biology
Ngā marau:	Machine learning Food traceability Microbiota 16S Manila clam North Adriatic sea Illegal unreported unregulated (IUU) fishing
Urunga tuihono:	https://doi.org/10.1186/s12915-024-02005-w
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Preventing illegal seafood trade using machine-learning assisted microbiome analysis

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