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Expansion of the Perfringens Predictor Model: to include pH, nitrites and salt concentrations in its predictions
Project Code: B13005
Institute of Food Research Enterprises Ltd
Peck, M ; Baranyi, J; Plowman, J; LeMarc, Y; Aldus, C; Munoz-Cuevas, M
Clostridium perfringens is a significant cause of food poisoning in the UK, and is found in low numbers in many foods, particularly meat and poultry. The spores can survive thermal processes used in the manufacture of meat products and grow to hazardous levels when these products are improperly cooled. Consumption of such products can lead to food poisoning.
In response to a key priority of the UK Food Standards Agency to reduce foodborne illness by 20% over a five-year period, the Institute of Food Research was commissioned in 2002 to develop a user-friendly software tool ‘Perfringens Predictor’ to predict the growth of C. perfringens during the heating/cooling of meats (Food Standards Agency project B14009). The software and user manuals were made freely available and downloadable from the Institute of Food Research web site (www.ifr.ac.uk/Safety/GrowthPredictor/default.html).
Aims and Objectives
The current version of the Perfringens Predictor model only allows the user to input temperature during cooling, and predictions are given under optimal conditions (pH 6.0 – 7.0, high water activity and no NaNO2). However, the food industry has indicated that the pH of meats could be lower and the NaCl concentrations as high as 3-4% with a concurrent reduction in water activity. Moreover, NaNO2 can be added up to a maximum concentration of 150 ppm. The purpose of this project was to expand Perfringens Predictor to include pH, NaCl and NaNO2 concentration. This will provide more accurate predictions. The project objectives were:
1. Develop a new dynamic model to predict growth of C. perfringens during the heating/cooling of meats that includes pH, NaCl and NaNO2.
2. Demonstrate that the new model provides a good prediction of the growth of C. perfringens during the heating/cooling of meats
3. Revise the user-friendly computer software tool to enable the user to input pH, NaCl, and NaNO2 concentration. The user manual will also be revised.
4. Make the revised version of Perfringens Predictor available free of charge within ComBase Predictor. Give presentations and make publications to ensure that the availability of the user-friendly computer software tool is widely known.
A new dynamic model has been developed for growth of C. perfringens during the cooling of bulked meats that includes pH (5.0–8.0), NaCl (0.5–4.0%) and NaNO2 concentration (0–150 ppm). The model is based on 84 growth curves and predicts growth within a range of 15°C-52°C. Twenty-two new growth curves were generated at IFR using a cocktail of five strains of C. perfringens. These strains were selected on the basis of rapid growth in beef slurry set at different pH/NaCl conditions during a heating/cooling treatment. A further 62 growth curves were extracted from the literature and ComBase.
The dynamic model was validated by comparison with literature data and with twenty new heating/cooling curves carried out at different pH/NaCl/NaNO2 conditions. The new validation data was generated using a cocktail of five hazardous strains of C.perfringens and different meats.
The user-friendly software tool (called Perfringens Predictor) has been revised to enable the user to input pH, NaCl (or water activity) and NaNO2 (cured or non-cured options. The user manual has also been revised. The software provides the user with a prediction of growth of C. perfringens under the specified dynamic cooling conditions, and with interpretation advice about predicted growth. This advice is automatically brought up after the predictions are displayed. Based on risk assessments carried out internationally, the Food Standards Agency recommends that the cooling of the meat should be sufficiently rapid to ensure that growth from heatresistant spores of C. perfringens is minimal (i.e. does not allow more than a one log increase). An improved web-based version of the software tool and user manual called Perfringens Predictor Web Edition is due to be launched in December 2006 and will be available at (http://ifrsvwwwdev.ifrn.bbsrc.ac.uk/ComBasePMP/PerfringensPredictor/Default.aspx). Several presentations have been made to highlight the availability of Perfringens Predictor and publication in peer reviewed journals is anticipated.
Relevance of Findings to Food Standards Agency Policy
A new improved dynamic predictive model has been developed and validated for predicting growth of C. perfringens during the cooling of bulked meats that includes pH, NaCl and NaNO2. It is suitable for use with cured meat products, and will allow more accurate prediction when pH and/or NaCl concentration are not optimal. Creating a web-based version of this program, that can be accessed via the internet, will allow Perfringens Predictor to be used more easily than the previous Excel download version, and should contribute to the Food Standards Agency aim to reduce the number of cases of foodborne illness associated with this organism.
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