Development of a mathematical protocol to graphically analyze irreversible changes induced by high pressure treatment in fish muscle proteins. (December 2017)

• Record Type: Journal Article
• Authors: Guzmán-Meza, M.; Segura-Ponce, L.; Tabilo-Munizaga, G.; Perez-Won, M.
• Is Part Of: Journal of food engineering. Volume 215(2017)
• Page Start: 134
• Page End: 146
• Publication Date: 2017-12
• Subjects: Mathematical protocol -- Protein denaturation -- Differential scanning calorimetry -- Fish protein -- High hydrostatic pressure; 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.2017.07.023
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: The aim of this study was to develop a mathematical protocol (MP) to graphically analyze the irreversible changes induced by high pressure treatment in fish muscle protein. The MP was validated by thermal analysis data (DSC) from pressurized samples of Seriolella violacea muscle tissue between 200 and 500 MPa for 2 s to 10 min. The MP included methodologies to obtain the thermodynamic parameters of protein denaturation and building 3-D plots from representative thermograms. Results obtained from the application of the MP showed that the pressurization of muscle tissue provoked that the total denaturation enthalpies values diminished, these changes were more evident for treatments >450 MPa. The irreversible denaturation of actin in the pressurized samples was observed for any treatment condition >300 MPa and greater than 3 min. The proposed MP is a powerful tool to improve data analysis from DSC when muscle tissue samples are subjected to pressurization. Highlights: Development of protocol to determine thermodynamic parameter of protein denaturation. Validation of the use of deconvolution on thermograms of Seriolella violacea. Interpolation of DSC data allows studying pressurized muscle protein behavior. The proposed MP is a powerful tool to improve data analysis from DSC.