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The distribution of log counts at a given time during the exponential growth phase of Listeria innocua measured in food samples inoculated with one cell each was applied to estimate the distribution of the single-cell lag times. Three replicate experiments in broth showed that the distribution of the log counts is a linear mapping of the distribution of the detection times measured by optical density. The detection time distribution reflects the lag time distribution but is shifted in time. The log count distribution was applied to estimate the distributions of the lag times in a liquid dairy product and in liver paté after different heat treatments. Two batches of ca. 100 samples of the dairy product were inoculated and heated at 55 degrees C for 45 min or at 62 degrees C for 2 min, and an unheated batch was incubated at 4 degrees C. The final concentration of surviving bacteria was ca. 1 cell per sample. The unheated cells showed the shortest lag times with the smallest variance. The mean and the variance of the lag times of the surviving cells at 62 degrees C were greater than those of the cells treated at 55 degrees C. Three batches of paté samples were heated at 55 degrees C for 25 min, 62 degrees C for 81 s, or 65 degrees C for 20 s. A control batch was inoculated but not heated. All paté samples were incubated at 15 degrees C. The distribution of the lag times of the cells heated at 55 degrees C was not significantly different from that of the unheated cells. However, at the higher temperatures, 62 degrees C and 65 degrees C, the lag duration was longer and its variance greater.

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To optimize the sanitation treatment of ready-to-eat (RTE) intermediate-moisture foods (IMF), the behavior of Listeria monocytogenes Scott A (CIP 103575), L. innocua (NTC 11288), Salmonella enterica serovar Typhimurium (CECT 443), and Escherichia coli O157:H7 (CECT 4972) following treatment with electron-beam irradiation has been studied. As food matrixes, three RTE vacuum-packed products (Iberian dry-cured ham, dry beef [cecina], and smoked tuna) were used. Although an irradiation treatment is not necessary when the 10(2) colony-forming units/g microbiological criterion for L. monocytogenes is applied, a treatment of 1.5 kGy must be applied to IMFs to meet the food safety objective in the case of the "zero tolerance" criterion for the three strains. The IMF products presented negligible modifications of color (L*, a*, and b*), sensory (appearance, odor, and flavor), and rheology (hardness, springiness, adhesiveness, cohesiveness, gumminess, chewiness, and breaking strength) parameters at doses lower than 2 kGy. Therefore, the treatment of 1.5 kGy warrants safe IMF with sensory properties similar to those of the genuine products.

Specific growth and death rates of Aeromonas hydrophila were measured in laboratory media under various combinations of temperature, pH, and percent CO 2 and O 2 in the atmosphere. Predictive models were developed from the data and validated by means of observations obtained from (i) seafood experiments set up for this purpose and (ii) the ComBase database (http://www.combase.cc; http://wyndmoor.arserrc.gov/combase/). Two main reasons were identified for the differences between the predicted and observed growth in food: they were the variability of the growth rates in food and the bias of the model predictions when applied to food environments. A statistical method is presented to quantitatively analyze these differences. The method was also used to extend the interpolation region of the model. Most predictive models in food microbiology focus on the specific growth and/or death rate (or the doubling time [D value]) of a microorganism as a function of the main environmental factors, such as temperature, pH, and others. These models are commonly based on observations made in a welldefined and controlled laboratory environment, using microbiological media. It is also vital to validate the predictions in food environments, which can be highly complex and sometimes difficult to characterize.The overall error of a model is defined by means of the mean square error (MSE) between predictions and observations made in food (19). If extrapolations are omitted from the predictions, as they should be, then the overall error refers only to the interpolation region. Sometimes, depending on the experimental design and available data, it is difficult to determine the interpolation region of a multivariate empirical model based purely on observations. Baranyi et al. (3) defined it as a minimum convex polyhedron (MCP), or convex hull, in the space of environmental factors. As Fig. 1 shows, the MCP encompasses those combinations of the environmental conditions for which observations were made to generate the model. Its vertices can be calculated as described previously (3). Model predictions outside the MCP are extrapolations.Often, conditions observed in food fall outside of the interpolation region but are close enough to it that they can be useful for model validation. These observations can also help to extend the interpolation region of the model.In this paper, we report new experimental data about the growth and death rates of Aeromonas hydrophila which vary with temperature, pH, and percent CO 2 and O 2 in the atmosphere. Both death and growth data were used to estimate the growth-no growth boundary of the organism. The growth data were used to generate a predictive growth model, which was then extensively validated by comparing its predictions with various observations in food. Some observations were outside of but close enough to the interpolation region of the growth model to be useful for the validation procedure. We developed an algorithm to extend the interpolation region of the model in order to utilize those origina...

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