A Predictive Model for the Bioaccumulation of Okadaic Acid in Mytilus galloprovincialis Farmed in the Northern Adriatic Sea: A Tool to Reduce Product Losses and Improve Mussel Farming Sustainability

Over the last decades, harmful dinoflagellate (Dinophysis spp.) blooms have increased in frequency, duration, and severity in the Mediterranean Sea. Farmed bivalves, by ingesting large amounts of phytoplankton, can become unsafe for human consumption due to the bioaccumulation of okadaic acid (OA),...

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Bibliographic Details
Published in:Sustainability 2023-05, Vol.15 (11), p.8608
Main Authors: Capoccioni, Fabrizio, Bille, Laura, Colombo, Federica, Contiero, Lidia, Martini, Arianna, Mattia, Carmine, Napolitano, Riccardo, Tonachella, Nicolò, Toson, Marica, Pulcini, Domitilla
Format: Article
Language:English
Subjects:
Agriculture
Algae
Algorithms
Aquaculture
Aquatic ecosystems
Bioaccumulation
Coastal zone
Coasts
Data collection
Diarrhetic shellfish poisoning
Dinoflagellates
Farmers
Farms
Harvesting
Low concentrations
Microorganisms
Mollusks
Mussels
Mytilus galloprovincialis
Okadaic acid
Phytoplankton
Prediction models
Remote sensing
Risk reduction
Shellfish
Shellfish farming
Sustainability
Toxins
Veterinary medicine
Water quality
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Summary:Over the last decades, harmful dinoflagellate (Dinophysis spp.) blooms have increased in frequency, duration, and severity in the Mediterranean Sea. Farmed bivalves, by ingesting large amounts of phytoplankton, can become unsafe for human consumption due to the bioaccumulation of okadaic acid (OA), causing Diarrhetic Shellfish Poisoning (DSP). Whenever the OA concentration in shellfish farmed in a specific area exceeds the established legal limit (160 μg·kg−1 of OA equivalents), harvesting activities are compulsorily suspended. This study aimed at developing a machine learning (ML) predictive model for OA bioaccumulation in Mediterranean mussels (Mytilus galloprovincialis) farmed in the coastal area off the Po River Delta (Veneto, Italy), based on oceanographic data measured through remote sensing and data deriving from the monitoring activities performed by official veterinarian authorities to verify the bioaccumulation of OA in the shellfish production sites. LightGBM was used as an ML algorithm. The results of the classification algorithm on the test set showed an accuracy of 82%. Further analyses showed that false negatives were mainly associated with relatively low levels of toxins (
ISSN:2071-1050
2071-1050
DOI:10.3390/su15118608