Application of chlorine dioxide and peroxyacetic acid during spray chilling as a potential antimicrobial intervention for beef carcasses.

Enteric pathogens such as Shiga-toxin producing Escherichia coli (STEC) and Salmonella spp. continue to be a major food safety concern for the beef industry. Currently, no single method is completely effective in controlling these pathogens during carcass processing.

MudPIT analysis of alkaline tolerance by Listeria monocytogenes strains recovered as persistent food factory contaminants.

Alkaline solutions are used to clean food production environments but the role of alkaline resistance in persistent food factory contamination by Listeria monocytogenes is unknown. We used shotgun proteomics to characterise alkaline adapted L. monocytogenes recovered as persistent and transient food factory contaminants.

Predicting growth rates and growth boundary of Listeria monocytogenes - An international validation study with focus on processed and ready-to-eat meat and seafood.

The performance of six predictive models for Listeria monocytogenes was evaluated using 1014 growth responses of the pathogen in meat, seafood, poultry and dairy products. The performance of the growth models was closely related to their complexity i.e.

The influence of non-lethal temperature on the rate of inactivation of vegetative bacteria in inimical environments may be independent of bacterial species.

The influence of non-lethal temperature on the survival of two species of food-borne bacteria under growth-preventing pH and water activity conditions was investigated. Specifically, inactivation rates of four strains of Escherichia coli and three strains of Listeria monocytogenes were determined in culture broth adjusted to pH 3.5 and water activity 0.

The future of predictive microbiology: strategic research, innovative applications and great expectations.

This paper considers the future of predictive microbiology by exploring the balance that exists between science, applications and expectations. Attention is drawn to the development of predictive microbiology as a sub-discipline of food microbiology and of technologies that are required for its applications, including a recently developed biological indicator. As we move into the era of systems biology, in which physiological and molecular information will be increasingly available for incorporation into models, predictive microbiologists will be faced with new experimental and data handling challenges.