Using an image processing based technique and predictive models for assessing lipid oxidation in rainbow trout fillet. (September 2017)

• Record Type: Journal Article
• Authors: Kamani, Mohammad Hassan; Safari, Omid; Mortazavi, Seyed Ali; Mehraban Sang Atash, Masoomeh; Azghadi, Naser Morgan
• Is Part Of: Food bioscience. Volume 19(2017:Sep.)
• Page Start: 42
• Page End: 48
• Publication Date: 2017-09
• Subjects: Image processing -- Rainbow trout -- Shelf-life prediction -- Oncorhynchus mykiss; Food -- Biotechnology -- Periodicals; Food -- Research -- Periodicals; Aliments -- Biotecnologia -- Revistes; Aliments -- Investigació -- Revistes; Food -- Biotechnology; Food -- Research; Revistes electròniques; Periodicals; 664.005
• DOI: 10.1016/j.fbio.2017.05.005
• Languages: English
• ISSNs: 2212-4292

Abstract

Abstract: The potential of image processing techniques in combination with multivariate regression analysis was studied for prediction of the lipid oxidation of stored trout fillets at 4 °C. Fillet images were acquired using an imaging system during the time intervals of 0, 5, 10, 15 and 20 days and the means of colorimetric parameters (L*, a*, b*, C* and h*) were obtained from the images. The multivariate regression models were built using a stepwise regression algorithm for prediction of the oxidative attributes. The models did well predicting the contents of FFA, PV and TBARS with the coefficient of determination (R 2 adjusted ) of 0.66, 0.78 and 0.94, respectively. Among all measured colorimetric parameters, hue angle (h*) and lightness (L*) showed more significant contributions in the modeling analyses. Overall, image processing techniques in combination with predictive models may possibly be useful as a precise and non-destructive method for assessment of oxidation in rainbow trout.