Modelling the interaction of the sakacin-producing Lactobacillus sakei CTC494 and Listeria monocytogenes in filleted gilthead sea bream (Sparus aurata) under modified atmosphere packaging at isothermal and non-isothermal conditions

Abstract

Journal of Food Microbiology © Elsevier after peer review. To access the final edited and published work see https://doi.Highlights 19 L. sakei CTC494 inhibited L. monocytogenes growth in sea bream fillets during 20 chilled and moderate abuse temperature storage. 21 L. sakei CTC494 did not increase deterioration of filleted sea bream at an initial 22 level of ≤ 4 log cfu/g. 23 L. sakei CTC494 showed potential as bioprotective culture for fish products. 24 An approach from broth to food was developed for modelling microbial 25 interaction. 26  Interaction models simulated L. monocytogenes inhibition by the bioprotective 27 L. sakei in filleted sea bream under static and dynamic temperature.28 29 30 31 32 33 34 35 36 37 Abstract 38 The objective of this work was to quantitatively evaluate the effect of Lactobacillus 39 sakei CTC494 (sakacin-producing bioprotective strain) against Listeria monocytogenes 40 in fish juice and to apply and validate three microbial interaction models (Jameson, 41 modified Jameson and Lotka Volterra models) through challenge tests with gilthead sea 42 bream (Sparus aurata) fillets under modified atmosphere packaging stored at isothermal 43 and non-isothermal conditions. L. sakei CTC494 inhibited L. monocytogenes growth 44 when simultaneously present in the matrix (fish juice and fish fillets) at different 45 inoculation ratios pathogen:bioprotector (i.e. 1:1, 1:2 and 1:3). The higher the 46 inoculation ratio, the stronger the inhibition of L. monocytogenes growth, with the ratio 47 1:3 yielding no growth of the pathogen. The maximum population density (N max ) was 48 the most affected parameter for L. monocytogenes at all inoculation ratios. According to 49 the microbiological and sensory analysis outcomes, an initial inoculation level of 4 log 50 cfu/g for L. sakei CTC494 would be a suitable bioprotective strategy without 51 compromising the sensory quality of the fish product. The performance of the tested 52 interaction models was evaluated using the Acceptable Simulation Zone approach. The 53 Lotka Volterra model showed slightly better fit than the Jameson-based models with 75-54 92 % out of the observed counts falling into the Acceptable Simulation Zone, indicating 55 a satisfactory model performance. The evaluated interaction models could be used as 56 predictive modelling tool to simulate the simultaneous behaviour of bacteriocin-57 producing Lactobacillus strains and L. monocytogenes; thus, supporting the design and 58 optimization of bioprotective culture-based strategies against L. monocytogenes in 59 minimally processed fish products.60 4

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

“…Then, a reduction ratio (α) was calculated to quantify the interaction effect on µ max by inoculated bacteria in co-culture experiments (Eq. 12) (Correia Peres Costa et al, 2019).…”

“…Two well-known interactions models for two-species were modified to predict the simultaneous growth of the threeinoculated spoilage bacteria in irradiated MP samples: the modified Jameson-effect model and the Lotka Volterra model (Cornu et al, 2011;Correia Peres Costa et al, 2019). As presented by Cornu et al (2011) and Quinto et al (2018), a modified generic primary growth model can be written as Eq.…”

“…During the last years, several models have been developed to predict the growth of SSOs in meat and meat products (Liu et al, 2006;Mataragas et al, 2006;Koutsoumanis, 2009;Kreyenschmidt et al, 2010;Dalcanton et al, 2013;Mejlholm and Dalgaard, 2013). But the majority of the developed models are based on the growth of two bacterial species in a food matrix (Vereecken et al, 2000;Giuffrida et al, 2007), most often to study the interaction between spoilage and pathogenic bacteria (Lebert et al, 2000;Mejlholm and Dalgaard, 2007;Giuffrida et al, 2009;Cornu et al, 2011;Ye et al, 2014;Correia Peres Costa et al, 2019;Pedrozo et al, 2019). Moreover, these models often describe the growth of the SSOs depending on the storage temperature (Dominguez and Schaffner, 2007;Gospavic et al, 2008;Kreyenschmidt et al, 2010;Psomas et al, 2011;Longhi et al, 2013;Antunes-Rohling et al, 2019) or the packaging conditions (Devlieghere et al, 1999;Chaix et al, 2015;Guillard et al, 2016;Couvert et al, 2019;Kapetanakou et al, 2019), but do not always consider the interaction of these storage conditions for the growth of spoilage bacteria (Rosso et al, 1995;Augustin and Carlier, 2000;Le Marc et al, 2002;Pinon et al, 2004;Dalcanton et al, 2018;Kakagianni et al, 2018;Nyhan et al, 2018;Correia Peres Costa et al, 2019).…”

“…As mentioned by Correia Peres Costa et al 2019: "interaction models are usually intended to quantify how much the growth of one population is reduced by the growth of other populations." In this context, two model approaches are generally used to describe the microbial interaction: (i) those based on the modified Jameson-effect phenomenon (Jameson, 1962;Cornu et al, 2011;Ye et al, 2014;Cauchie et al, 2017;Correia Peres Costa et al, 2019), and (ii) those based on the predator-prey models (Lotka Volterra equation) (Dens et al, 1999;Berlow et al, 2004;Powell et al, 2004;Giuffrida et al, 2007;Mounier et al, 2008;Cornu et al, 2011;Ye et al, 2014;Correia Peres Costa et al, 2019).…”

“…However, this hypothesis cannot always be applied in food matrix. Indeed, the growth of a bacterium (B A ) presents simultaneously with other bacteria in a food matrix (B B and B C ) can be affected by three different ways: (i) B A growth with a reduced growth rate after that B B and B C reach their maximal population densities (N max , expressed in log CFU/g), (ii) B A stops growing when B B and B C reach their N max , and (iii) B A declines when B B and B C reach their N max (Cauchie et al, 2017;Correia Peres Costa et al, 2019). It could be so interesting to develop a Lotka Volterra model for a three-species approach, by considering the effect of the natural food microbiota for the growth of specific spoilage bacteria.…”