Modeling of sodium nitrite and water transport in pork meat. (May 2019)

• Record Type: Journal Article
• Authors: Gómez, J.; Sanjuán, N.; Arnau, J.; Bon, J.; Clemente, G.
• Is Part Of: Journal of food engineering. Volume 249(2019)
• Page Start: 48
• Page End: 54
• Publication Date: 2019-05
• Subjects: Modelling -- Nitrite -- Water -- Diffusion -- Pork meat; 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.2019.01.008
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Four models were used to simulate nitrite uptake and water loss during pork meat curing with sodium nitrite: three empirical ones (the Azuara, the Peleg and the Zugarramurdi and Lupin) and one theoretical (the diffusional). By means of the Azuara and the Peleg models, the equilibrium moisture content and the equilibrium nitrite content were properly identified. Zugarramurdi and Lupin's model did not provide information about process parameters. The effective diffusivities of water (Dwe ) and nitrite (DNe ) were calculated. The activation energy (ENa and Ewa ) was evaluated from the parameters of both the Peleg and the diffusional models. The results were similar; the Peleg model having the advantage of simplicity of calculation. The effect of meat anisotropy was confirmed from the diffusional model; the perpendicular transport of nitrite is easier than the parallel. This study highlighted the importance of choosing the most appropriate model depending on the objective to be achieved. Highlights: Nitrite diffusion coefficient perpendicular to meat fibers is higher than that of nitrate. Nitrite and water diffusion during meat curing is influenced by direction of meat fibers. Activation energy for water and nitrite diffusion determined from the parameters of both the Peleg and diffusional models was similar. It is important to consider the objective of the study when choosing the most appropriate model for the curing process.