Microbiological hazards and safe process design manufacturer and that use of these instructions results in high product quality Good control of heat processing and hygiene in the factory and the home or food service outlet are essential for product safety. The prevention of product re- contamination or cross-contamination after heating plays an even more critical role when products are sold as ready-to-eat It is essential that foods relying on chilled storage for their safety are stored at or below the specified temperature(s)(from -1 to +8C)during manufacture, distribution and storage. Storage at higher temperatures can allow the growth of any hazardous micro-organisms that may be present. Inappropriate processing in conjunction with temperature or time abuse during storage will certainly lead to the growth of spoilage micro-organisms and premature loss of quality. Labuza and Bin-Fu(1995)have proposed the use of time/temperature integrators(TTI)for monitoring the conditions and the extent of temperature abuse in the distribution chain. In conjunction with predictive microbial kinetics the impact of storage temperature on the safe shelf-life of meat and poultry products can be estimated The risks associated with any particular products can be investigated either by practical trials(such as challenge testing) or by the use of mathematical modelling The use of predictive models for microbial killing by heat (interchange of ime and temperature to calculate process lethality based on D and values)or the extent of microbial growth can improve supply chain management. In the Uk,FoodMicromodel(fmm:www.ifra.co.uk)andintheUs,thePathogen ModellingProgram(www.arserrc.gov/mfs/regform.htm)arecomputer-based predictive microbiology databases applicable to chilled products. Panisello and Quantick, (1998)used FMM to make predictions on the growth of pathogens in response to variations in the ph and salt content of a product and specifically the effect of lowering the pH of pate. Zwietering and Hasting(1997)have taken this oncept a stage further and developed a modelling approach to predict the ffects of processing on microbial growth during food production, storage and distribution. Their process models were based on mass and energy balances ogether with simple microbial growth and death kinetics and were evaluated using a meat product line and a burger processing line. Such models can predict the contribution of each individual process stage to the microbial level in a product Zwietering et al.(1991)and Zwietering et al. (1994a, b)have modelled the impact of temperature and time and shifts in temperature during processing on the growth of Lactobacillus plantarum. Such predictive models can, in principle be used for suggesting the conditions needed to control microbial growth or the extent of the microbial 'lag'phase during processing and distribution where temperature fluctuations may be common and could allow growth. Impe et al.(1992) have also built similar models describing the behaviour of bacterial populations during processing in terms of both time and temperature, but have extended their models to cover inactivation at temperatures above the maximum temperature for growth Adair and Briggs (1993)have proposed the development of expert systems, based on predictive models to assess the microbiological safety of chilled foodsmanufacturer and that use of these instructions results in high product quality. Good control of heat processing and hygiene in the factory and the home or food service outlet are essential for product safety. The prevention of product re￾contamination or cross-contamination after heating plays an even more critical role when products are sold as ready-to-eat. It is essential that foods relying on chilled storage for their safety are stored at or below the specified temperature(s) (from
1º to +8ºC) during manufacture, distribution and storage. Storage at higher temperatures can allow the growth of any hazardous micro-organisms that may be present. Inappropriate processing in conjunction with temperature or time abuse during storage will certainly lead to the growth of spoilage micro-organisms and premature loss of quality. Labuza and Bin-Fu (1995) have proposed the use of time/temperature integrators (TTI) for monitoring the conditions and the extent of temperature abuse in the distribution chain. In conjunction with predictive microbial kinetics the impact of storage temperature on the safe shelf-life of meat and poultry products can be estimated. The risks associated with any particular products can be investigated either by practical trials (such as challenge testing) or by the use of mathematical modelling. The use of predictive models for microbial killing by heat (interchange of time and temperature to calculate process lethality based on D and z values) or the extent of microbial growth can improve supply chain management. In the UK, Food MicroModel (FMM: www.lfra.co.uk) and in the US, the Pathogen Modelling Program (www.arserrc.gov/mfs/regform.htm) are computer-based predictive microbiology databases applicable to chilled products. Panisello and Quantick, (1998) used FMM to make predictions on the growth of pathogens in response to variations in the pH and salt content of a product and specifically the effect of lowering the pH of paˆte´. Zwietering and Hasting (1997) have taken this concept a stage further and developed a modelling approach to predict the effects of processing on microbial growth during food production, storage and distribution. Their process models were based on mass and energy balances together with simple microbial growth and death kinetics and were evaluated using a meat product line and a burger processing line. Such models can predict the contribution of each individual process stage to the microbial level in a product. Zwietering et al. (1991) and Zwietering et al. (1994a, b) have modelled the impact of temperature and time and shifts in temperature during processing on the growth of Lactobacillus plantarum. Such predictive models can, in principle, be used for suggesting the conditions needed to control microbial growth or indicate the extent of the microbial ‘lag’ phase during processing and distribution where temperature fluctuations may be common and could allow growth. Impe et al. (1992) have also built similar models describing the behaviour of bacterial populations during processing in terms of both time and temperature, but have extended their models to cover inactivation at temperatures above the maximum temperature for growth. Adair and Briggs (1993) have proposed the development of expert systems, based on predictive models to assess the microbiological safety of chilled foods. Microbiological hazards and safe process design 289