Predictive modelling of antimicrobial effects of natural aromatic compounds in model and food systems.

Abstract

This chapter describes various approaches in the predictive modelling of the antimicrobial effects of natural aromatic compounds in model and food systems. The applications of primary models (growth models and survival models) and secondary models in the antimicrobial assessment of essential oil effects are covered. Future perspectives on the application of mathematical models in the assessment of the antimicrobial activities of essential oils are briefly discussed.

Antibacterial activity of Thymus capitatus and Thymus algeriensis essential oils against four food‐borne pathogens inoculated in minced beef meat

“…However, the complexity and variability of EOs make it difficult, in general, to correlate their antimicrobial activities to a specific compound (Belletti, Kamdem, Lanciotti, & Gardini, ). In general, EOs are slightly more active against Gram‐positive than Gram‐negative bacteria (Aldana, Andrade‐Ochoa, Aguilar, Contreras‐Esquivel, & Nevarez‐Moorillon, ; Delaquis, Stanich, Girard, & Mazza, ).…”

“…However, the complexity and variability of EOs make it difficult, in general, to correlate their antimicrobial activities to a specific compound (Belletti, Kamdem, Lanciotti, & Gardini, 2011 On the other hand, the previous study demonstrated that the strong antibacterial activity of thyme EOs might be due to its major constituents: thymol and carvacrol. Both of thymol and carvacrol appear to make the cell membrane permeable and are able to disintegrate the outer membrane of Gram-negative bacteria (Burt, 2004).…”

Characterization of Mexican Coriander (Eryngium foetidum) Essential Oil and Its Inactivation of Listeria monocytogenes In Vitro and during Mild Thermal Pasteurization of Pineapple Juice

“…The Weibull equation was used to fit the survivor data for each thermal treatment, as follows: log S (t)= -bf (2) where S(t) is the survival ratio (N,/N0), t is the time of treatment, and n and b are temperature-dependent parameters that represent the shape factor and the rate parameter related to the location parameter, respectively (43). The shape factor n determines the presence of a shoulder (concave downward when n > 1), a tail (concave upward for n < 1), or a straight line (n = 1, the firstorder kinetic).…”