Food Authenticity Models for Mytilus galloprovincialis (Mediterranean Mussel): Exploratory Study

Geographical origin determination for seafood products is a fundamental aspect due to its implications for fraud prevention, ensuring food safety, and promoting resource sustainable management. In this research, different machine learning (ML) models based on random forests, support vector machines, and artificial neural networks were fed with trace element fingerprinting (TEF) and stable isotope ratio analysis (SIRA) to determine the origin of mussels that have been farmed in eight regions and ten locations around the world (areas of the European Atlantic coast, the Mediterranean Sea, and the Pacific coast of Chile). Fourteen trace elements in shells and carbon and nitrogen isotope ratios of mussel tissue were used singly, in combination, or reduced to develop the different approach models. All the selected models present high prediction accuracies for the independent variables (except for SIRA models), for their combination, or for their optimisation, highlighting the artificial neural network and random forest models that presented a 100% accuracy for all cases using a combination of variables selected based on a random forest model TEF to predict region and location, respectively. This fact confirms that ML models are suitable approximation techniques to determine the region and location of Mediterranean mussel origin, with key applications in food safety and global sustainability.