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VIDEOS

Combatting seafood fraud: Application of machine learning to develop chemometric differentiation tools using metal isotope ratios and chemical contaminants in eastern oysters

Seafood fraud is a pervasive global issue, resulting in billions of dollars of lost revenue each year from the global food industry. In the U.S., an example of seafood fraud involves trafficking oysters and mislabeling them as different types of oysters with higher value, which undermines environmental management measures and interferes with customers’ decisions. In this study, we aim to develop chemometric models that would link oysters to their geographic origin, which would aid law enforcement in identifying and combatting seafood fraud. We will leverage previous contaminant analyses of historically collected oyster samples from the NOAA NS&T Mussel Watch Program. A subset of archived and freshly collected oyster samples will additionally be analyzed for their strontium, lead, zinc, and copper isotopic compositions, which may act as geographic markers. Through application of machine learning tools, we will determine whether the NS&T Mussel Watch Program dataset contains enough discriminatory information to accomplish highly accurate geographic discrimination, or whether the addition of isotope ratio measurements would improve the accuracy of the chemometric models.

Uploaded On

November 30, 2023