Identifying environmental factors associated with tetrodotoxin contamination in bivalve mollusks using eXplainable AI

Since 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safety risks and economic losses, making early prediction of TTX contamination vital to the food industry and competent authorities. Recent studies have pointed to shallow habitats and water temperature as main drivers to TTX contamination in bivalve mollusks. However, the temporal relationships between abiotic factors, biotic factors, and TTX contamination remain unexplored. We have developed an explainable, deep learning-based model to predict TTX contamination in the Dutch Zeeland estuary. Inputs for the model were meteorological and hydrological features; output was the presence or absence of TTX contamination. Results showed that the time of sunrise, time of sunset, global radiation, water temperature, and chloride concentration contributed most to TTX contamination detection. Thus, the photoperiod (time of sunset/sunrise) and solar irradiance (global radiation), were identified as potential drivers for tetrodotoxin contamination in bivalve mollusks. To conclude, our explainable deep learning model identified environmental factors to be associated with TTX contamination in bivalve mollusks, supporting its suspected exogenous origin. These findings make our approach a valuable tool to mitigate marine toxin risks for food industry and competent authorities.