High Throughput Shelf Life Determination of Atlantic Cod (Gadus morhua L.) by Use of Hyperspectral Imaging

Abstract

Fish quality and shelf life can be evaluated using various assessment methods, such as sensory analysis, biochemical tests, microbiological evaluations, and physicochemical analyses. However, these methods are invasive and time-consuming, driving interest in technologies capable of estimating shelf life through non-invasive procedures. This study investigates the potential of hyperspectral imaging as a non-invasive technology for predicting the shelf life of Atlantic cod. A storage experiment was conducted that included both gutted fish with heads (GFWH) and fillets, with sensory evaluation and biochemical measurements employed to determine shelf life. Subsequently, hyperspectral images of the fish samples were captured under industrial production conditions, and the spectral data were analyzed using different regression algorithms. The majority of the regression techniques utilized in this research successfully predicted shelf life for both fillets and GFWH, achieving a root mean square error (RMSE) lower than one day. While most regression models exhibited comparable performance in predicting the shelf life of fillets, deep learning-based models demonstrated superior performance for GFWH. These results suggest that hyperspectral imaging technology has significant potential as a non-invasive tool for estimating the shelf life of Atlantic cod, thereby enabling effective quality-based sorting, reducing food waste, and enhancing sustainability in the seafood supply chain.