(76c) Predictive Modelling of Minimal Food Processing Techniques

In recent years, the focus of food microbiology has been shifting from simply ensuring food safety and extending its shelf life to finding minimal processing techniques to preserve key food characteristics such as nutritional content, taste, texture and natural colour (Shah et al., 2019; Troy et al., 2016). Among novel minimal treatments that are being studied for the deactivation of harmful pathogens and food spoilage organisms are natural antimicrobials (e.g., nisin), ultrasound and cold atmospheric plasma (CAP) (Costello et al., 2019, 2018). The efficacy of each of these treatments is dependent on many factors such as food type, concentration (in the case of antimicrobials) or intensity of treatment as well as storage conditions (i.e. temperature and/or protective atmosphere). Moreover, treatments can be combined to achieve the desired level of decontamination (Berdejo et al., 2019; Ross et al., 2003). These aspects pose substantial challenges due to the range of potential food products, bacterial pathogens, treatments and conditions that must be explored to find optimal processing strategies while ensuring minimal detriment to food characteristics.

Most previous studies focused on the inactivation of pathogens by natural antimicrobials such as nisin, applied individually or in combination with ultrasound or CAP, have been conducted on real foods or in laboratory broths (for example, Koda et al., 2009; Stratakos and Grant, 2018). However, studies in real food products are useful only for the system studied, and liquid broth models do not account for structural effects of solid-like foods (Smet et al., 2018).

Our previous contributions addressed this issue through the development of a range of monophasic and biphasic food model systems using Xanthan gum (XG) and/or Whey protein isolate (WPI) and/or fat that mimic a variety of real food products and are stable at a range of temperatures. The advantage of these model food systems consists in high reproducibility of their structural and rheological properties that can also be readily tuned to mimic those of a wide variety of real food products.

Systematic studies of the combined effects of artificially added nisin and food model structure under different temperatures on the growth kinetics and spatial organization of Listeria revealed the strong influence of microscopic structural features (i.e., the distribution of the protein-polysaccharide phases) on colony size and distribution, apart from the more readily observable population-scale growth rate of the bacteria (Costello et al., 2019, 2018).

Despite the growing amount of evidence on the efficacy of different treatment strategies the available data and growth models for specific cases reported thus far are lacking a predictive capability across different foods, treatments and storage conditions.

In this work we develop a predictive phenomenological model describing the growth of Listeria in a unified formulation across a wide range of food products under a variety of conditions including treatments with nisin, ultrasound and CAP as well as temperatures. The main differentiating feature of this approach is that the effects of structural composition and corresponding rheological properties are incorporated into the model. In addition, the modelling approach can be readily extended to other pathogenic bacteria using a relatively small set of quantitative experimental observations under specific conditions.

The predictive modelling framework has the potential to accelerate the development of novel microbial decontamination treatments for food products, better assess food condition based on storage history as well as optimise storage and distribution protocols to ensure food safety and minimise costs.

References

Berdejo, D., Pagán, E., García-Gonzalo, D., Pagán, R., 2019. Exploiting the synergism among physical and chemical processes for improving food safety. Curr. Opin. Food Sci. 30, 14–20.

Costello, K., Gutierrez-Merino, J., Bussemaker, M., Ramaioli, M., Baka, M., Van Impe, J.F., Velliou, E.G., 2018. Modelling the microbial dynamics and antimicrobial resistance development of Listeria in viscoelastic food model systems of various structural complexities. Int. J. Food Microbiol. 286, 15–30.

Costello, K., Gutierrez-Merino, J., Bussemaker, M., Smet, C., Van Impe, J.F., Velliou, E.G., 2019. A multi-scale analysis of the effect of complex viscoelastic models on Listeria dynamics and adaptation in co-culture systems. AIChE J. e16761.

Koda, S., Miyamoto, M., Toma, M., Matsuoka, T., Maebayashi, M., 2009. Inactivation of Escherichia coli and Streptococcus mutans by ultrasound at 500 kHz. Ultrason. Sonochem. 16, 655–659.

Ross, A.I.V., Griffiths, M.W., Mittal, G.S., Deeth, H.C., 2003. Combining nonthermal technologies to control foodborne microorganisms. Int. J. Food Microbiol. 89, 125–138.

Shah, U., Ranieri, P., Zhou, Y., Schauer, C.L., Miller, V., Fridman, G., Sekhon, J.K., 2019. Effects of cold plasma treatments on spot-inoculated Escherichia coli O157:H7 and quality of baby kale (Brassica oleracea) leaves. Innov. Food Sci. Emerg. Technol. 57.

Smet, C., Baka, M., Dickenson, A., Walsh, J.L., Valdramidis, V.P., Van Impe, J.F., 2018. Antimicrobial efficacy of cold atmospheric plasma for different intrinsic and extrinsic parameters. Plasma Process. Polym. 15, 1–12.

Stratakos, A.C., Grant, I.R., 2018. Evaluation of the efficacy of multiple physical, biological and natural antimicrobial interventions for control of pathogenic Escherichia coli on beef. Food Microbiol. 76, 209–218.

Troy, D.J., Shikha, K., Kerry, J.P., Tiwari, B.K., 2016. Sustainable and consumer-friendly emerging technologies for application within the meat industry : An overview. Meat Sci. 120, 2–9.