| Popis: |
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 ( |
