Regularization Methods for High-Dimensional Data as a Tool for Seafood Traceability

• Published in: Journal of statistical theory and practice Vol. 17; no. 3
• Main Authors: Yokochi, Clara, Bispo, Regina, Ricardo, Fernando, Calado, Ricardo
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-09-2023
• Subjects: Ecological Statistics; Mathematics and Statistics; Original Article; Probability Theory and Stochastic Processes; Statistical Theory and Methods; Statistics; Ridge regression; Traceability; Elastic net; Regularization; LASSO
• ISSN: 1559-8608; 1559-8616
• DOI: 10.1007/s42519-023-00341-8

Seafood traceability, needed to regulate food safety, control fisheries, combat fraud, and prevent jeopardizing public health from harvesting in polluted locations, depends heavily on the prediction of the geographic origin of seafood. When the available datasets to study traceability are high-dimensional, standard classic statistical models fail. Under these circumstances, proper alternative methods are needed to predict accurately the geographic origin of seafood. In this study, we propose an analytical approach combining the use of regularization methods and resampling techniques to overcome the high-dimensionality problem. In particular, we analyze comparatively the
Ridge regression
, LASSO and
Elastic net
penalty-based approaches. These methods were applied to predict the origin of the saltwater clam
Ruditapes philippinarum
, a non-indigenous and commercially very relevant marine bivalve species that occurs commonly in European estuaries. Further, the resampling method of
Monte Carlo Cross-Validation
was implemented to overcome challenges related to the small sample size. The results of the three methods were compared. For fully reproducibility, an R Markdown file and the used dataset are provided. We conclude highlighting the insights that this methodology may bring to model a multi-categorical response based on high-dimensional dataset, with highly correlated explanatory variables, and combat the mislabeling of geographic origin of seafood.