Assessing the Growth of Listeria monocytogenes in Mediterranean Fish Products from Marine Aquaculture

Introduction: Predictive microbiology is an important tool, which has immediate applications to improve microbial food safety and quality. Although several mathematical models have been developed to predict microbial behaviour in seafood and fishery products, most of them were performed on ready-to-eat (RTE) products from Nordic or Atlantic species. Therefore, there is a need for the development of a predictive models for Mediterranean fish species that considers the effects of packaging technologies and storage conditions, on both spoilage and pathogenic bacteria.

Purpose:  The objective of the present study was to evaluate the growth kinetics of Listeria monocytogenes in the range 4-20ºC, at different atmospheric conditions, in fish-based juice (FBJ) of gilthead sea bream (Sparus aurata).

Methods:  Validation was performed with experimental growth data from the pathogens in fresh sea bream and sea bass (Dicentrarchus labrax) fillets from marine aquaculture. Then, predictions were assessed with external data from scientific literature. Models were compared with predictions from other existing tertiary models for L. monocytogenes growth included in the software Food Spoilage and Safety Predictor (FSSP).

Results: Validation with experimental data from challenge testing showed that models generated in FBJ slightly over-predicted growth rates, providing fail-safe predictions (B_f = 2.03). A perfect coincidence between predictions and observations was observed in the case of the FSSP's model (B_f = 1.00). The validation process of the FBJ model developed under anaerobic conditions demonstrated that the model was able to adequately described growth rates observed, by other authors, in different fresh fish species (B_f, A_f= 1.33).

Significance:  This study provides useful information about L. monocytogenes growth in Mediterranean fish species through the development of predictive models, which might be applied in the food industry. It also demonstrates that the use of food-based systems to evaluate microbial behaviour can be a suitable instrument to obtain accurate predictive models.