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Scientific
Publications - Work Done by Microbiology Reader
Mike Peck, Predicting the behaviour of food bacterial pathogens, Institute of Food Research, Food Safety Theme, Norwich Research Park, Colney, Norwich, UK, website, 1 pp. PARTIAL TEXT
Tools to underpin safe food production Increasingly, foods are being sold which have received minimal preservation treatments. We are developing new mathematical modelling tools to help ensure the continued safe development of these foods. IFR is internationally known for fundamental work on modelling, and translating this knowledge into software programs and advice to assist industry.
Understanding and predicting pathogen response to stresses Our established expertise with foodborne clostridia meets stakeholder needs, in that we are quantifying combinations of environmental factors that provide the required protection factor for foods with respect to Clostridium botulinum and C. perfringens. This includes the development of predictive models. A crucial issue for food safety is the lag time from spores of C. botulinum, which is variable and often difficult to predict. We aim to develop a better understanding of factors influencing lag time, provide better predictions, and hence ensure safety with respect to the foodborne botulism hazard. To achieve this we are developing an image analysis system to measure the duration of distinct stages of lag, and are also measuring total lag time using a Bioscreen. We are also establishing datasets on the variability of the lag time for Listeria, C. botulinum and E. coli, work partially supported by the EU through project BACANOVA. Current work is focused on the effect of pre-treatments (such as heating) on the distribution of the lag times of individual cells, and these data will inform our modern stochastic modelling approach for predictive microbiology. We are using advanced bioinformatics tools to develop a relational database for microbiological data and are co-ordinating the international effort to populate it. 'ComBase' will be available on the Internet this year for industry, regulators and the research community, and is well suited to dealing with issues relating to quantitative risk assessment. Developed in collaboration with the ARS laboratory in Wyndmoor, Pennsylvania, USA it contains over 20,000 examples of microbial response (e.g.growth, death) to food environments. Microbial response to food environment can be influenced by food structure. Examples of the effect of structure include the adhesion of cells to plant tissue and the local chemical environment. We have shown that if bacteria are immobilised within a food, they grow colonially, and that accumulation of metabolic end products within colonies of Salmonella Typhimurium induces an acid tolerance response. Optimised cells have cross-resistance to inorganic acid and water activity - hence potentially improved survival.
Quantitative risk assessments for complex hazards We are developing user-friendly mathematical descriptions, and improved quantitative understanding, for the hazards and risks that are associated with food consumption and examining the roles of population variability and knowledge uncertainty in relation to foodborne illness. Communication and management of food safety information can then be handled with more clarity and confidence. We have explored some hazards in detail, particularly those associated with spore forming bacteria in minimally processed foods, and have also promoted the use of powerful new methods such as Bayesian Belief modelling. The models extend beyond process descriptions to concepts such as anti-microbial resistance or emergence that have prominent roles to play in a holistic view of food safety. All are developed with users in mind, particularly non-experts, and are supported by modern dynamic communication, so that quantitative understanding about food safety can be shared and appreciated by all.
Better methods for the detection and characterisation of pathogens A key issue for stakeholders is rapid, specific identification of pathogens. Two recent, collaborative IFR examples are a Listeria monocytogenes-specific microplate ELISA, based on an antibody raised to Internalin, a Listeria virulence protein, and a multianalyte dipstick to detect all verotoxin-producing E. coli (including E. coli O157). An LFIA (lateral-flow immunoassay) device was developed which allows simultaneous detection of verotoxin and E. coli O157.
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