Date of Award

2021

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Department

Biological Sciences

First Advisor

Dr Michael Callanan

Second Advisor

Dr Máire Begley

Third Advisor

Dr Nick Johnson

Abstract

Listeria monocytogenes is a foodborne pathogen which is a significant challenge in food production, particularly for ready-to-eat (RTE) products. Incidence of Listeria in RTE foods can be reduced by the application of intelligent combinations of preservative factors or hurdles, while data quantifying the efficacy of hurdle combinations can be exploited and used in the area of predictive microbiology. Traditional culture-based techniques, such as viable plate counts, are commonly used to monitor the growth/survival of microorganisms in foods, however these methods are tedious, time-consuming and due to their destructive nature, are associated with low reproducibility and high variability. Therefore, rapid, non-invasive enumeration methods are required as alternatives to culture-based techniques for studying the growth kinetics of Listeria in food matrices in response to various hurdles. The overall aim of this thesis was to investigate novel methods for predicting, monitoring and controlling the growth of Listeria in complex foods. A mathematical model was validated which predicted the combinatorial effect of pH (4.7 – 5.3), water activity (0.93 – 0.98) and organic acids (0 – 2mM) on Listeria growth in laboratory media (BHI broth) and two RTE foods (zucchini purée and béarnaise sauce). Experimental findings highlighted the importance of model validation in real food matrices, while the study was the first to report on modelling of propionic acid in combination with other hurdles for inhibition of Listeria. Following this, the efficacy of indirect impedance (using the Rapid Automated Bacterial Impedance Technology; R.A.B.I.T. system) for measuring growth rates of L. innocua in the afore-mentioned food matrices under similar environmental conditions was investigated. Although growth rates were determined in several different experimental conditions in all three test matrices, growth of L. innocua was not detected in a large number of conditions, highlighting the limitations associated with the technology for determining microbial growth rates. Subsequently, Lux technology was evaluated as an alternative to traditional plate counts for determining growth rates of Listeria in foods under ix varying conditions of pH, water activity and organic acids. Results showed that specific growth rates determined using Lux technology were not significantly different from those obtained using plate counts, highlighting the potential of the method as a rapid alternative to plating techniques. The combinatorial effect of bioengineered nisin derivatives on Listeria growth was explored, with enhanced anti-listerial activity observed when peptides were used in combination with each other, compared to when each was used singly. Importantly, the enhanced activity of the selected nisin peptide combinations was maintained in a model food system. Finally, the use of ultraviolet light-emitting diodes (UV-LEDs) as an alternative to traditional mercury UV lamps for the inactivation of L. monocytogenes and three other microorganisms (Escherichia coli, Salmonella Typhimurium and Bacillus subtilis) on a plastic surface and in powdered food ingredients was investigated, with results highlighting the efficacy of the method and its potential for future application in the food industry. In conclusion, this work has investigated the use of predictive tools, novel antimicrobial strategies and alternative enumeration methods with the overall aim of controlling the growth of Listeria in foods and thus, improving food safety.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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info:eu-repo/semantics/closedAccess

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