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2000. This paper describes a system for the microbiological quantitative risk assessment for food products and their production processes. The system applies a stepwise risk assessment, allowing the main problems to be addressed before focusing on less important problems. First, risks are assessed broadly, using order of magnitude estimates. Characteristic numbers are used to quantitatively characterize microbial behaviour during the production process. These numbers help to highlight the major risk-determining phenomena, and to ®nd negligible aspects. Second, the risk-determining phenomena are studied in more detail. Both general and/or speci®c models can be used for this and varying situations can be simulated to quantitatively describe the risk-determining phenomena. Third, even more detailed studies can be performed where necessary, for instance by using stochastic variables. The system for quantitative risk assessment has been implemented as a decision supporting expert system called SIEFE: Stepwise and Interactive Evaluation of Food safety by an Expert System. SIEFE performs bacterial risk assessments in a structured manner, using various information sources. Because all steps are transparent, every step can easily be scrutinized. In the current study the effectiveness of SIEFE is shown for a cheese spread. With this product, quantitative data concerning the major risk-determining factors were not completely available to carry out a full detailed assessment. However, this did not necessarily hamper adequate risk estimation. Using ranges of values instead helped identifying the quantitatively most important parameters and the magnitude of their impact. This example shows that SIEFE provides quantitative insights into production processes and their risk-determining factors to both risk assessors and decision makers, and highlights critical gaps in knowledge.

Clostridium perfringens is a pathogen that mainly causes food poisoning outbreaks when large quantities of food are prepared. Therefore, a model was developed to predict the effect of different cooling procedures on the growth of this pathogen during cooling of food: Dutch pea soup. First, a growth rate model based on interpretable parameters was used to predict growth during linear cooling of pea soup. Second, a temperature model for cooling pea soup was constructed by fitting the model to experimental data published earlier. This cooling model was used to estimate the effect of various cooling environments on average cooling times, taking into account the effect of stirring and product volume. The growth model systematically overestimated growth of C. perfringens during cooling in air, but this effect was limited to less than 0.5 log N/ml and this was considered to be acceptable for practical purposes. It was demonstrated that the growth model for C. perfringens combined with the cooling model for pea soup could be used to sufficiently predict growth of C. perfringens in different volume sizes of pea soup during cooling in air as well as the effect of stirring, different cooling temperatures, and various cooling environments on the growth of C. perfringens in pea soup. Although fine-tuning may be needed to eliminate inaccuracies, it was concluded that the combined model could be a useful tool for designing good manufacturing practices (GMP) procedures.

Samples (351) of ice-cream were examined for 'total aerobic' colony count, Enterobacteriaceae, coliforms and Escherichia coli. Different methods for the indicator groups were compared. A number of the samples were also examined for Staphylococcus aureus, Bacillus cereus and Salmonella spp. In addition samples were tested microbiologically to detect fraudulently added inhibitory substances. A percentage (3 1.1) of the samples showed a total count < 103/ml; 11.0% contained z 105/ml, which is the limit laid down in the Dutch Food Law. The MPN of coliforms (without pre-enrichment) was < I/ml in 46.7% of the samples; 7.4% showed MPNs > 103/ml. Corresponding figures for Enterobacteriaceae (with pre-enrichment) were 25.4% and 16.8% respectively. A percentage (33.0) did not meet the present Dutch standard for coliforms. The figures for samples that did not meet the standards of the Food Law are somewhat higher than those found by the Food Inspection Departments, probably because the latter generally investigate more samples from large factories. Staph. aureus and B. cereus were found only sporadically. None of 36 samples, selected because they contained appreciable numbers of indicator organisms, was found to contain salmonellas. Of 100 samples 86% showed inhibitory properties to one or more test micro-organisms. There was no correlation between a positive test and microbial quality. Sometimes, however, the flavouring agent (lemon and chocolate) seemed to exert an inhibitory activity. The hygienic quality of ice-cream prepared in large factories was better than that of the other samples. The poor quality sometimes reported for 'soft' ice-cream was not confirmed in our investigations. A typically favourable influence of flavour on bacteriological quality could be demonstrated only for lemon ice-cream. The same values for MPNs of Enterobacteriaceae were found when overnight pre-enrichment in buffered peptone water was replaced by 2 h resuscitation in tryptone soya broth. Resuscitation for only 45 min in peptone saline yielded lower results. When followed by an Enterobacteriaceae colony count, overnight pre-enrichment, 2 h resuscitation in tryptone soya broth or 1 h resuscitation on tryptone soya agar in Petri dishes gave almost the same results. N o differences were found in favour of MPNs when compared with colony counts of Enterobacteriaceae. The method found most efficient for the detection of Esch. coli in ice-cream relies on resuscitation followed by enrichment in brilliant green bile lactose broth at 44°C.