Shelf life prediction of expired vacuum-packed chilled smoked salmon based on a KNN tissue segmentation method using hyperspectral images. (June 2016)

• Record Type: Journal Article
• Authors: Ivorra, Eugenio; Sánchez, Antonio J.; Verdú, Samuel; Barat, José M.; Grau, Raúl
• Is Part Of: Journal of food engineering. Volume 179(2016:Jun.)
• Page Start: 110
• Page End: 116
• Publication Date: 2016-06
• Subjects: Hyperspectral imaging -- Tissue segmentation -- SW-NIR -- Fish shelf life -- Smoked salmon -- SVM; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2016.01.008
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Ready-to-eat foods that does not receive a heat treatment before being consumed can be at risk of foodborne hazards and spoilage, so it would be of great interest to have a method for monitoring their safety. This work expands on and enhances previous successfully studies with hyperspectral imaging in the SW-NIR range. Specifically, a k-nearest-neighbours model was developed to classify the salmon tissue into white myocommata stripes (fat) and muscle (lean) tissue. Partial Least Squares models developed confirm that a spatial segmentation should be performed before a shelf life model can be calculated. Employing the fat spectra and only the 7 most correlated wavelengths, a support vector machine model was calculated to classify into days 0, 10, 20, 40 and 60 with 87.2% prediction accuracy. These results make the method developed very promising as a non-destructive method to analyse the shelf life of vacuum-packed chilled smoked salmon fillets. Highlights: Hyperspectral information in the range of SW-NIR was used to assess salmon shelf life. Packed salmon samples were analysed without manipulation. Segmentation of fish tissues (lean and fat) using Knn model. iPLS selected the 7 most correlated wavelengths (visible and NIR) with shelf life.