The USDA Food Safety & Inspection Service (FSIS) and the USDA Agricultural Research Service (ARS) have joined together to produce the Predictive Microbiology Information Portal (PMIP). This portal is geared to assist  food companies (large and small) in the use of predictive models, the appropriate application of models, and proper model interpretation. This vision is that the PMIP will be the highway to the most comprehensive websites that brings together large and small food companies in contact with the information needed to aid in the production of the safest foods. The PMIP links users to numerous and diverse resources associated with models (PMP), databases (ComBase) , regulatory requirements, and food safety principles.

A dynamic model for the growth of Salmonella spp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to 15 °C; 600 h and 10 to 40 °C; 52 h) sinusoidal, continuously varying temperature profiles. LWE adjusted to pH 7.8 was inoculated with approximately 2.5–3.0 log CFU/mL of Salmonella spp., and the growth data at several isothermal conditions (5, 7, 10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45, and 47 °C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. 

Pulsed light (PL) is a fast non-thermal method for microbial inactivation. This research studied the kinetics of PL inactivation of microorganisms naturally occurring in some vegetables. Iceberg lettuce, white cabbage and Julienne-style cut carrots were subjected to increasing PL fluences up to 12 J/cm² in order to study its effect on aerobic mesophilic bacteria determined by plate count. Also, sample temperature increase was determined by infrared thermometry. Survivors’ curves were adjusted to several models. 

An attempt to use a Bayesian approach to model variability and uncertainty separately in microbial growth in a risk assessment is presented. It was conducted within the framework of a French project aiming at assessing the exposure to Listeria monocytogenes in cold-smoked salmon. The chosen model describes the effect of time and temperature on bacterial growth. A Bayesian approach close to the one proposed by Pouillot et al. [Int. J. Food Microbiol. 81 (2003) 87] is used to estimate the variability and uncertainty of growth parameters from both literature data and data experimentally acquired during the project. Variability between strains and between products is taken into account. The growth of the food flora of cold-smoked salmon is also modelled by the same method. 

This article discusses the growth and inactivation models that can be used in a stepwise procedure for quantitative risk assessment. The proposed stepwise procedure provides a structured method of risk assessment and prevents the researcher from getting caught in too much complexity. This simplicity is necessary because of the complex nature of food safety. The principal aspects are highlighted during the procedure and many factors can be omitted since their quantitative effect is negligible.

A mathematical model was developed to predict time to inactivation (TTI) by high pressure processing of Listeria monocytogenes in a broth system (pH 6.3) as a function of pressure (450 to 700 MPa), inoculum level (2 to 6 log CFU/ml), sodium chloride (1 or 2%), and sodium lactate (0 or 2.5%) from a 4°C initial temperature. Ten L. monocytogenes isolates from various sources, including processed meats, were evaluated for pressure resistance. The five most resistant strains were used as a cocktail to determine TTI and for model validation. 

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, the researchers in this article built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events.

In this article, the researchers developed and validated secondary models that can predict growth parameters of Salmonella Typhimurium and Staphylococcus aureus in cooked-pressed ready-to-eat (RTE) pork as a function of concentrations (0 to 3%) of a commercial potassium lactate and sodium diacetate mixture (PL+SDA) and temperature (10 to 30°C). The primary growth data were fitted to a Gompertz equation to determine the lag time (LT) and growth rate (GR). Model performance was also evaluated by use of the prediction bias (Bf) and accuracy (Af) factors, median relative error, and mean absolute relative error, as well as the acceptable prediction zone method.

The purpose of this study was to develop a mathematical model to describe the growth of Salmonella on cut tomatoes at various temperatures. Four Salmonella serotypes (Typhimurium, Newport, Javiana, and Braenderup) obtained from previous tomato-linked cases of salmonellosis were used in this study. 

Prevalence and counts of Salmonella Typhimurium in fresh pork sausage packs at the point of retail were modeled by using Irish and United Kingdom retail surveys' data. A methodology for modeling a second-order distribution for the initial Salmonella concentration (λ₀) in pork sausage at retail was presented considering the uncertainty originated from the most probable-number (MPN) serial dilutions.

A stochastic simulation modelling approach was taken to determine the extent of Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line.

In this paper, the authors developed probability distribution functions for the change in concentration of E. coli O157:H7 in cider using data from scientific publications for use in a quantitative risk assessment.

This study used classical parametric survival modeling to derive parametric distributions from the RTI International storage practices data set, which was conducted as a national survey of U.S. adults to characterize consumers' home storage and refrigeration practices for 10 different categories of refrigerated ready-to-eat foods.

Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated bylactic acid production (product inhibition).

The SSSP software predicts shelf-life and growth of bacteria in different fresh and lightly preserved seafood, e.g. the effect of product temperature profiles recorded during storage and distribution by data loggers. Some of the predictive models in SSSP are equally useful for other types of food. The new version features a Listeria monocytogenes model.

In this study, transfer of L. monocytogenes from one contact surface to another for Ready-to-Eat deli meats with a delicatessen or restaurant type slicer was investigated. The objectives were to achieve the surface transfer model development and to predict the cross-contamination for the slicing operation. Two cross-contamination routes were studied for model development: (1) L. monocytogenes–contaminated blade to ham (Case I), and (2) L. monocytogenes–contaminated ham to blade and then to uncontaminated ham (Case II).

The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.

To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50°C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (?), specific growth rate (?), and the 95% prediction interval (PI). Secondary models for ?, ?, and PI as a function of storage temperature were developed and then combined with the primary model to create a tertiary model. Performance of the tertiary model was evaluated against dependent data, independent data for interpolation, and independent data for extrapolation to kosher chicken skin by using an acceptable prediction zone from ?1 (fail-safe) to 0.5 (fail-dangerous) log per skin portion.

This study quantified and modeled the survival of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella Typhimurium in soudjouk-style fermented sausage during fermentation, drying, and storage. The objectives of this study were to quantify the survival of E. coli O157:H7, L. monocytogenes, and Salmonella Typhimurium in a soudjouk-style sausage during fermentation and drying to various pH and aw values and at various storage temperatures, and to describe the survival using mathematical equations to estimate the survivability of these three pathogens in other FDSS products.

This report details a mathematical simulation model describing cross contamination pathways for L. monocytogenes. A smoked fish processing plant was used for the model. The report concludes that 10.7% of foods in a lot are contaminated, and that the most significant contributors to contamination were the frequency in which employees' gloves touched food and food surfaces, and the frequency of changing gloves. Access to the full report requires purchase.

This study uses Monte Carlo simulation techniques to quantitatively describe the cross-contamination of Listeria monocytogenes. Two models are presented. The first model describes the number and prevalence for four different strains while the second model describes the number and prevalence for only one strain. The purpose of these models is to provide a mathematical framework for future predictive models with the ultimate goal of understanding and controlling L. monocytogenes in food-processing plants.

Database of predictive microbiology information collected from researchers, institutions, and published literature. The database may be searched based on temperature, pH, water activity, condition, source (publication), organism, and environment. Files are returned giving organism, maximum rate, doubling time or D-value, source, conditions, environment, temperature, pH, water activity, a table and chart for log concentration vs. time, and any further details. A basic registration process also allows a user to download the data