Home style frying of steak and meat products: Survival of Escherichia coli related to dynamic temperature profiles.

Microbial survival of heating and cross-contamination are the two transmission routes during food preparation in the consumers' kitchen that are relevant for QMRA (Quantitative Microbial Risk Assessment). The aim of the present study was to extend the limited amount of data on microbial survival during real-life preparation of meat and meat products and to obtain accessory temperature data that allow for a more general (product unspecific) approach. Therefore survival data were combined with extensive measurements of time- and location dependent temperature using an infrared camera for the surface and buttons for the inside of the product, supplemented with interpolation modelling. We investigated the survival of heating of Escherichia coli O111:H2 in beefsteak, hamburgers (beef and 50% beef 50% pork (HH)), meatballs (beef and HH) and crumbs (HH). For beefsteak, survival as a whole is dominated by the sides, giving a log reduction of 1-2 (rare), 3-4 (medium) and 6-7 (done). Limited measurements indicated that done preparation gave 5-6 log reduction for crumbs and at least 8-9 log for the other products. Medium preparation gave a higher reduction in hamburgers (2-4 log) than in meatballs (1-2 log) and in beef (3-4) than in HH (2-3) hamburgers. In general, our 'done' results give larger inactivation than found in literature, whereas 'rare' and 'medium' results are similar. The experiments resulted in two types of curves of D70/z-values, dependent on product, doneness and for beefsteaks sides vs. top/bottom. One type of curve agrees reasonably with literature D70/z estimates from isothermal temperature experiments, which supports using these estimates for home style cooking QMRA calculations. In case of the other type of curve, which is mainly found for (near) surface contamination in close contact with the pan, these literature estimates cannot be applied. We also applied a simplified approach, assuming thermal inactivation is dominated by the highest temperatures reached. The time duration of this highest temperature gives accessory D-values which prove to fit with isothermal temperature literature data, thus suggesting application of such data for QMRA is possible by this approach also, which is less labor intensive both in terms of measurements and modelling. In real life, variability in product properties and preparation styles is large. Further studies are needed to analyze the effect on survival, preferably focusing on determining the essential variables. More variation in heating time will allow for estimating D70/z point estimates rather than curves representing possible sets of D70/z-values.

Modelling of inactivation through heating for quantitative microbiological risk assessment (QMRA).

EFSA regards the household as a stage in the food chain that is important for the final number of food-borne infections. The fate of a pathogen in the private kitchen largely depends on consumer hygiene during preparation of food and on its proper cooking, especially in the case of meat. Unfortunately, detailed information on the microbiological survival in meat products after heating in the consumer kitchen is lacking. The aim of the study was to improve the estimation of the inactivating effect on pathogens by heating meat or a meat product by the consumer in the kitchen. On that account, artificially contaminated meat and meat products were cooked according to several degrees of doneness and simulating real world conditions, and bacterial survival was measured. Heat camera pictures and button temperature loggers inserted into the food matrix served to record time and the temperature of heating. Temperature, time and the microbial survival ratio observed served to inform a mathematical model able to explain the thermal inactivation of meat or a meat product in home settings. The results of the study would help to improve microbiological comparative exposure assessments of pathogens in food, as an attribution tool and as a supportive tool for risk-based sampling in monitoring and surveillance.