Diarrhetic shellfish poisoning toxins (DSTs) are biotoxins produced by several dinoflagellates Dinophysis species. Their accumulation in shellfish, especially in mussels, may pose significant public health issues. To protect consumers from diarrhetic shellfish poisoning (DSP), when mussels having DSTs higher than regulatory limits are detected, production areas are typically closed temporarily, and harvested stock need to be discarded, causing a financial burden on the industry. Predicting the occurrence of DSTs contamination in mussels will not only help with harvest timing management but also improve public health safety. Previously, a Bayesian Networks (BNs) model has been developed which can provide short-term prediction on DSP toxins variation in mussels from Bantry Bay, Ireland(Wang et al., 2022). The model's inputs were weekly plankton density in seawater, DSP toxin concentration in mussels from ten production sites in Bantry Bay, and sea surface temperature. In this study,  environmental factors (including wave period, sea temperature, and air temperature) were added into the BNs model to assist better prediction accuracy. The model was re-trained with data from 2014 to 2018 and validated with data from 2019. Validation results showed a better performance than the previous BNs model, which is higher than 90%. For the class of DSP concentration higher than the regulation limits, the prediction accuracy is improved from 73% to 87%.   

Primary Presenter: Xiyao Wang, University College Dublin (wang.xiyao@ucdconnect.ie)

Authors:

Xiyao Wang, University College Dublin  (wang.xiyao@ucdconnect.ie)

Yamine Bouzembrak, Wageningen Food Safety Research  ()

Hans Marvin, Wageningen Food Safety Research  ()

Dave Clark, Marine Institute Ireland  ()

Francis Butler, University College Dublin  ()