(536f) The Effect of Fat Concentration on the Microbial Dynamics and Bacterial Biofilm Development on a Novel Viscoelastic Triphasic Food Model

According to the World Health Organisation (WHO) about 600 million – almost one in ten people in the world – get bacterial foodborne diseases every year from consuming contaminated food. This enormously high number indicates the importance of ensuring that food safety is achieved in the contest of food industry. Bacteria, however, play a key role in food production for many different reasons. For example, some food related bacteria are beneficially used as starter cultures in the dairy industry, spoilage bacteria cause organoleptic changes to the food product and pathogenic bacteria are harmful and can lead to severe disease or death. To achieve microbiologically safe food products, it is important to understand the bacterial dynamics, especially in relation to their environment (both intrinsic and external). Most existing microbiological studies in food systems focus on single cultures, but in reality bacteria exist in co-cultures in a real food system. More specifically, multiple bacteria usually co-exist and live in beneficial communities, so-called biofilms. Their behaviour in such three dimensional biofilm structures is very different to single cultures. (El Kadri et al., 2020; Moons, Michiels and Aertsen, 2009; Fox et al., 2014; Zilelidou and Skandamis, 2018).

There are several aspects that can affect the success of food safety both intrinsic to the food, i.e., chemical and structural food components and extrinsic, i.e., the processing/preservation techniques applied and the types of potential contaminants. Food products are generally very complicated biochemically, structurally and biophysically. Therefore, to obtain a fundamental understanding of intra-species interactions and the effect of the food environment (whether intrinsic or extrinsic), it is important to design simpler in vitro models as compared to real food products. Most quantitative studies on food related microbial kinetics (growth or inactivation) in in vitro models are conducted in liquid broths and very few in simple mono-phase homogenous gel systems (with gelling agents such as agar, gelatine, xanthan gum etc.) (Fröhling et al., 2012; Baka et al., 2017; Fei et al., 2020, Noriega et al., 2013; Velliou et al., 2013). Those studies have showed that the bacterial behaviour is different in liquid systems compared to solid systems, as the availability of nutritional components and oxygen changes and so does the bacterial communication (Wimpenny et al., 1995; Smet et al., 2017; Heir et al., 2018). Most food products are structured or solid-like and it is therefore important to account for more a complex composition and structural properties of food models to conduct microbiological research.

In this study, building on our recently developed biphasic protein/polysaccharide food model (Costello et al., 2018, 2019), we have developed novel triphasic food models enriched with fat, as studies in real food have revealed that the fat concentration has a significant impact on the bacterial behaviour (Brocklehurst and Wilson, 2000; Skandamis and Nychas, 2012).

METHODS

Our novel triphasic systems consist of Xanthan gum, Whey protein and Vegetable oil. Various oil concentrations in the range 10-60% were considered (range similar to real food products). A microscopic comparison showed that these novel triphasic systems are structurally similar to real foods; like soft cheeses or meat patés.

Quantitative analysis of the microbial dynamics on the tri-phase systems with oil concentration of 0%, 20% and 60% was conducted. More specifically, the growth of the foodborne gram-positive pathogen Listeria monocytogenes, the gram-negative pathogen Escherichia coli, and the spoilage bacteria Pseudomonas aeruginosa was monitored at 37 °C, while the growth of the starter organism Lactococcus lactis was monitored at 30 °C. Furthermore, advanced confocal laser scanning microscopy (CLSM) was used for the spatial determination of the food model components and for the analysis of the distribution of bacterial colonies on the food models.

For preliminary evaluation of the biofilm formation, the biofilm formation of the above strains was quantified and classified via a crystal assay based on a method by Stepanović et al. (2000). The analysis was carried out with different initial inoculum sizes (10⁸ & 10³ CFU/mL), temperatures (25 °C, 30 °C & 37 °C) and incubation times (3 days, 5 days, 7 days and 10 days) on the surface of a polystyrene well plate.

RESULTS

Generally, the oil concentration in the triphasic model did not affect the macroscale/macroscopic microbial growth kinetics (as evaluated by the Baranyi Roberts (1994) growth model). However, at the microscopic scale, generally, in the first hours of mid-exponential/early-stationary phase the bacterial colonies reduced in size with increasing oil concentration. Higher oil concentration resulted in space limitations and diffusional limitations of nutrients, as bacteria are not able to digest lipids. Furthermore, in terms of growth location most bacterial colonies/aggregates were located close to the oil droplets. Due to these microscopic differences, different levels of cell-cell and colony-colony interactions take place for different structural configurations of our models. This can allow bacteria to send signalling molecules, such as the so-called quorum sensing molecules which can lead to different levels of stress adaptation. Additionally, the size of the colonies can have a direct impact on the efficiency of mild preservation methods. The biofilm analysis showed that the a lower inoculum size of 10³ CFU/mL, when incubated at 30 °C for 3 or 5 days results in higher/stronger biofilm formation. These preliminary data can be used as control standards for the biofilm formation on the food model systems as well as for appropriate and robust design of biofilms on the models.

CONCLUSIONS

In conclusion, our results indicate the importance of accounting for food biochemical composition and microstructural complexities when monitoring food related bacteria. A multi-level complex analysis on both macroscale and microscale enables a better prediction of bacterial interactions: whilst the macroscopic microbial kinetics can be unaffected by small environmental changes, on a microscale level small alterations of the environment can result in substantial changes in the bacterial interactions, which can further impact the efficiency and the design of food decontamination treatments.

ACKNOWLEDGEMENTS

This work was supported by the Department of Chemical and Process Engineering of the University of Surrey as well as the Doctoral College of the University of Surrey. E.V. is grateful to the Royal Academy of Engineering for an Industrial Fellowship.

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