Statistical modelling of seafood fraud in the Canadian supply chain

Abstract

AbstractSeafood misrepresentation, encompassing product adulteration, mislabelling, and substitution, among other fraudulent practices, has been rising globally over the past decade, greatly impacting both the loss of important fish species and the behaviour of human consumers alike. While much effort has been spent attempting to localise the extent of seafood mislabelling within the supply chain, strong associations likely existing among key players have prevented timely management and swift action within Canada and the USA in comparison to European nations. To better address these shortcomings, herein frequentist and Bayesian logistic Generalised Linear Models (GLMs) are developed in R and Stan for estimation, prediction and classification of product mislabelling in Metro Vancouver, British Columbia, Canada. Obtained results based on odds ratios and probabilities paint a grim picture and are consistent with general trends found in past studies. This work paves the way to rapidly assess the current state of knowledge surrounding seafood fraud nationally and on a global scale using established statistical methodology.