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

Abstract

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 (<100 μg·kg−1), since the algorithm tended to classify low concentrations of OA as negative samples, while true positives had higher mean values of toxins (139 μg·kg−1). The results of the model could be used to build up an online early warning system made available to shellfish farmers of the study area, aimed at increasing the economic and environmental sustainability of these production activities and reducing the risk of massive product losses.